Offline angle selection in rotational imaging and tracking systems

ABSTRACT

A processing device determines a plurality of angles from which tracking images can be generated by an imaging device. The processing device generates a plurality of projections of a treatment planning image of a patient, the treatment planning image comprising a delineated target, wherein each projection of the plurality of projections has an angle that corresponds to one of the plurality of angles from which the tracking images can be taken. The processing device determines, for each angle of the plurality of angles, a value of a tracking quality metric for tracking the target based on an analysis of a projection generated at that angle. The processing device selects a subset of the plurality of angles that have a tracking quality metric value that satisfies a tracking quality metric criterion.

FIELD

This patent specification relates to rotational imaging and trackingsystems, and in particular to angle selection for rotational imaging andtracking systems.

BACKGROUND

Pathological anatomies such as tumors and lesions can be treated with aninvasive procedure, such as surgery, which can be harmful and full ofrisks for the patient. A non-invasive method to treat a pathologicalanatomy (e.g., tumor, lesion, vascular malformation, nerve disorder,etc.) is external beam radiation therapy, which typically uses aradiation treatment source (e.g., a linear accelerator (LINAC)) togenerate radiation beams such as x-rays. In one type of external beamradiation therapy, a radiation treatment source (also referred to hereinas a therapeutic radiation source) directs a sequence of x-ray beams ata tumor site from multiple angles, with the patient positioned so thetumor is at the center of rotation (isocenter) of the beam. As the angleof the therapeutic radiation source changes, every beam passes throughthe tumor site, but passes through a different area of healthy tissue onits way to and from the tumor. As a result, the cumulative radiationdose at the tumor is high and that to healthy tissue is relatively low.

The term “radiosurgery” refers to a procedure in which radiation isapplied to a target region at doses sufficient to treat a pathology infewer treatment stages (also known as treatment fractions or simplyfractions) than with delivery of lower doses per fraction in a largernumber of treatment stages. Radiosurgery is typically characterized, asdistinguished from radiotherapy, by relatively high radiation doses perfraction or treatment stage (e.g., 500-2000 centiGray), extendedtreatment times per fraction (e.g., 30-60 minutes per treatment), andhypo-fractionation (e.g., one to five fractions). Radiotherapy istypically characterized by a low dose per fraction or treatment stage(e.g., 100-200 centiGray), shorter fraction times (e.g., 10 to 30minutes per treatment) and hyper-fractionation (e.g., 30 to 45fractions). For convenience, the term “radiation treatment” is usedherein to mean radiosurgery and/or radiotherapy (also referred to asx-ray therapy and radiation therapy) unless otherwise noted.

Image-guided radiation therapy (IGRT) systems include gantry-basedsystems and robotic arm-based systems. In gantry-based systems, a gantryrotates the therapeutic radiation source around an axis passing throughthe isocenter. Gantry-based systems include C-arm gantries, in which thetherapeutic radiation source is mounted, in a cantilever-like manner,over and rotates about the axis passing through the isocenter.Gantry-based systems further include ring gantries having generallytoroidal shapes in which the patient's body extends through a bore ofthe ring/toroid, and the therapeutic radiation source is mounted on theperimeter of the ring and rotates about the axis passing through theisocenter. Robotic arm-based systems include a robotic arm to which thetherapeutic radiation source is mounted.

Associated with each radiation therapy system is an imaging system toprovide in-treatment images (referred to herein as tracking images) thatare used to set up and, in some examples, guide the radiation deliveryprocedure and track in-treatment target motion. Portal imaging systemsplace a detector opposite the therapeutic radiation source to image thepatient for setup and in-treatment images, while other approachesutilize distinct, independent image radiation source(s) and detector(s)for the patient set-up and in-treatment images. Tracking imagesgenerated at some angles may be better suited to target tracking thantracking images generated at other angles. However, it can be difficultto determine which angles will produce the optimal target trackingperformance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a radiation treatment environment according toembodiments.

FIG. 2 illustrates a method of selecting a set of angles for use by arotational imaging device to be used during treatment, in accordancewith one embodiment of the present invention.

FIG. 3A illustrates a first method of determining tracking qualitymetric values for angles of a rotational imaging device, in accordancewith one embodiment of the present invention.

FIG. 3B illustrates a second method of determining tracking qualitymetric values for angles of a rotational imaging device, in accordancewith one embodiment of the present invention.

FIG. 3C illustrates a third method of determining tracking qualitymetric values for angles of a rotational imaging device, in accordancewith one embodiment of the present invention.

FIG. 3D illustrates a fourth method of determining tracking qualitymetric values for angles of a rotational imaging device, in accordancewith one embodiment of the present invention.

FIG. 4 illustrates a method of selecting a set of angles for use by arotational imaging device to be used during treatment, in accordancewith one embodiment of the present invention.

FIG. 5 illustrates a method of selecting a set of angles for use by arotational imaging device to be used during treatment, in accordancewith one embodiment of the present invention.

FIG. 6A illustrates a method of selecting a set of angles for use by arotational imaging device to be used during treatment of a movingtarget, in accordance with one embodiment of the present invention.

FIG. 6B illustrates a method of determining tracking quality metricvalues for angles based on a motion model, in accordance with oneembodiment of the present invention.

FIG. 7 illustrates a method of selecting a set of angles for use by arotational imaging device during a treatment stage, in accordance withone embodiment of the present invention.

FIG. 8 illustrates a method of using a set of angles by a rotationalimaging device during a treatment stage, in accordance with oneembodiment of the present invention.

FIG. 9A illustrates a first method of selecting a set of angles for useby a rotational imaging device during a treatment stage, in accordancewith one embodiment of the present invention.

FIG. 9B illustrates a second method of selecting a set of angles for useby a rotational imaging device during a treatment stage, in accordancewith one embodiment of the present invention.

FIG. 9C illustrates a third method of selecting a set of angles for useby a rotational imaging device during a treatment stage, in accordancewith one embodiment of the present invention.

FIG. 10A illustrates an axial cut-away view of a gantry basedimage-guided radiation treatment (IGRT) system according to a oneembodiment.

FIG. 10B illustrates a side cut-away views of the gantry based IGRTdelivery system of FIG. 10A, according to one embodiment.

FIG. 10C illustrates a perspective view of a rotatable gantry structureof the IGRT delivery system of FIGS. 10A-10B, according to oneembodiment.

FIG. 11 illustrates a perspective view of a gantry based IGRT deliverysystem and a schematic diagram of a computer system integral therewithand/or coupled thereto, according to one embodiment.

FIG. 12 illustrates a perspective view of a robotic arm based IGRTdelivery system, according to a one embodiment.

FIG. 13 illustrates example tracking quality metric values for 72 testedangles, according to a one embodiment.

FIG. 14 illustrates an example patient along with angles that can beused to successfully track a target in the patient and angles thatcannot be used to successfully track the target, according to a oneembodiment.

DETAILED DESCRIPTION

Embodiments of the present invention are directed to methods and systemsfor selecting angles (referred to as imaging angles) to be used by animaging system to generate tracking images of a target during atreatment stage. Imaging systems with movable imaging devices may beable to generate tracking images from many different angles. However,some angles may be superior to other angles for target trackingpurposes. For example, at some imaging angles a target that is to betreated may not be discoverable and/or may be indistinguishable fromother structures. At other imaging angles, target tracking algorithmsmay incorrectly identify a non-target structure as the target. Selectionof optimal angles for generating tracking images is non-trivial, and canhave important consequences in a treatment stage. Image angle selectioncan be of particular importance for image-guided radiation therapy.

In one embodiment, a processing device determines a plurality of anglesfrom which tracking images can be generated by an imaging device. Theprocessing device generates a plurality of projections of athree-dimensional treatment planning image of a patient, such as acomputer tomography (CT) scan of the patient, a magnetic resonanceimaging (MRI) scan of the patient, or a three-dimensional image of thepatient based on another three-dimensional imaging modality. Thethree-dimensional treatment planning image includes a delineated target,wherein each projection of the plurality of projections has an anglethat corresponds to one of the plurality of angles from which thetracking images can be taken. The processing device determines, for eachangle of the plurality of angles, a value of a tracking quality metricfor tracking the target based on an analysis of a projection generatedat that angle. The processing device selects a subset of the pluralityof angles that have a tracking quality metric value that satisfies oneor more tracking quality metric criteria. The selected angles maycorrespond to the optimal angles to use for generating tracking imagesthat can be used to track the target during a treatment stage. In oneembodiment, at least a first angle in the subset has a separation of atleast 15 degrees from a second angle in the subset.

In one embodiment, a processing device determines a set of angles thathave a tracking quality metric value that satisfies one or more trackingquality metric criteria. The processing device may be a component of aradiation therapy apparatus (also referred to herein as an image-guidedradiation treatment (IGRT) apparatus) that includes a gantry thatrotates at a speed of greater than one rotation per minute during atreatment stage. During an alignment phase or a treatment phase of atreatment stage, multiple operations may be performed to track a target.At least a first angle and a second angle may be selected from the setof angles for a first rotation of the gantry. An imaging device mountedto the gantry may then generate a first tracking image of the targetfrom the first angle during the first rotation of the gantry.Subsequently, the imaging device may generate a second tracking image ofthe target from the second angle during the first rotation of thegantry. The processing device may perform target tracking based on thefirst tracking image and the second tracking image.

Referring now to the figures, FIG. 1 illustrates a radiation treatmentenvironment 100 within which one or more embodiments as discussed hereinmay be applied. The radiation treatment environment 100 includes areference imaging system 102 and an IGRT delivery system 104. Referenceimaging system 102 usually comprises a high precision volumetric imagingsystem such as a computed tomography (CT) system or a nuclear magneticresonance imaging (MRI) system. In view of cost and workflowconsiderations in many clinical environments, the reference imagingsystem 102 is often a general purpose tool used for a variety ofdifferent purposes in the clinic or hospital environment, and is notspecifically dedicated to the IGRT delivery system 104. Rather, thereference imaging system 102 is often located in its own separate roomor vault and is purchased, installed, and/or maintained on a separateand more generalized basis than the IGRT delivery system 104.Accordingly, for the example of FIG. 1, the reference imaging system 102is illustrated as being distinct from the IGRT delivery system 104.Notably, for other radiation treatment environments that are not outsidethe scope of the present teachings, the reference imaging system 102 canbe considered as an integral component of the IGRT delivery system 104.

A treatment planning system 118 receives the imaging data from thereference imaging system 102 and performs one or more treatment planningoperations. Treatment planning system 118 includes a processing device170 to generate and modify treatment plans and/or simulation plans.Processing device 170 may represent one or more general-purposeprocessors (e.g., a microprocessor), special purpose processor such as adigital signal processor (DSP) or other type of device such as acontroller or field programmable gate array (FPGA). Processing device170 may be configured to execute instructions for performing simulationgenerating operations and/or treatment planning operations discussedherein.

Treatment planning system 118 may also include system memory 177 thatmay include a random access memory (RAM), or other dynamic storagedevices, coupled to processing device 170 by a bus, for storinginformation and instructions to be executed by processing device 170.System memory 177 also may be used for storing temporary variables orother intermediate information during execution of instructions byprocessing device 170. System memory 177 may also include a read onlymemory (ROM) and/or other static storage device coupled to the bus forstoring static information and instructions for processing device 170.

Treatment planning system 118 may also include A storage device 180,representing one or more storage devices (e.g., a magnetic disk drive,optical disk drive, solid state drive, etc.) coupled to the bus forstoring information and instructions. Storage device 180 may be used forstoring instructions for performing the treatment planning stepsdiscussed herein, such as treatment planning operations to select a setof imaging angles.

Processing device 170 may also be coupled to a display device, such as acathode ray tube (CRT) or liquid crystal display (LCD), for displayinginformation (e.g., a 2D or 3D representation of the VOI) to a user. Aninput device, such as a keyboard, may be coupled to processing device170 for communicating information and/or command selections toprocessing device 170. One or more other user input devices (e.g., amouse, a trackball or cursor direction keys) may also be used tocommunicate directional information, to select commands for processingdevice 170 and to control cursor movements on the display.

Treatment planning system 118 may share its database (e.g., data storedin storage 180) with IGRT delivery system 104, so that it may not benecessary to export from the treatment planning system 118 prior totreatment delivery. Treatment planning system 118 may be linked to IGRTdelivery system 104 via a data link, which may be a direct link, a LANlink or a WAN link. It should be noted that when data links areimplemented as LAN or WAN connections, any of reference imaging system102, treatment planning system 118 and/or IGRT delivery system 104 maybe in decentralized locations such that the systems may be physicallyremote from each other. Alternatively, any of reference imaging system102, treatment planning system 118, and/or IGRT delivery system 104 maybe integrated with each other in one or more systems.

In common clinical practice, treatment planning is performed on apre-acquired treatment planning image 106 generated by the referenceimaging system 102. The pre-acquired treatment planning image 106 isoften a high resolution three-dimensional CT scan image acquiredsubstantially in advance (e.g., one to two days in advance) of the oneor more radiation treatment fractions that the patient will undergo. Thepre-acquired treatment planning image 106 may also be a four-dimensionalCT scan image. As indicated in FIG. 1 by the illustration of an (i, j,k) coordinate system for the pre-acquired treatment planning image106,which is in contrast to the (x, y, z) treatment room coordinate systemillustrated for the treatment room of the IGRT delivery system 104,there is generally no pre-existing or intrinsic alignment orregistration between the treatment planning image 106 coordinate systemand the treatment room coordinate system.

During the treatment planning process, a physician establishes acoordinate system (e.g., i, j, k in treatment planning image 106) withinthe treatment planning image, which may also be referred to herein asthe planning image coordinate system or planning image reference frame.A radiation treatment plan is developed in the planning image coordinatesystem that dictates the various orientations, sizes, durations, etc.,of the high-energy treatment radiation beams to be applied by theradiation treatment source 108 during each treatment fraction or stage.Accurate delivery of therapeutic radiation to a target includes aligningthe planning image coordinate system with the treatment room coordinatesystem, as the entire delivery and tracking system (if present) iscalibrated to the treatment room coordinate system. It will beappreciated that this alignment does not need to be exact and furtherappreciated that couch adjustment or beam delivery adjustment can beused to account for offsets in the alignment between the two coordinatesystems.

Thus, immediately prior to each treatment fraction (also referred to asa treatment stage), under a precise image guidance of the imagingdevices 110, according to one or more of the embodiments describedfurther hereinbelow, the patient is physically positioned such that theplanning image coordinate system (defined, for example and not by way oflimitation, by a physician while creating a treatment plan on a CT imageor planning image) is positioned into an initial alignment with thetreatment room coordinate system, hereinafter termed an initialtreatment alignment or initial treatment position. This alignment iscommonly referred to as patient set up. Depending on the location of thetarget volume, the target volume can vary in position and orientationand/or can undergo volumetric deformations due to patient movementand/or physiological cycles such as respiration.

As used herein, the term in-treatment alignment variation orin-treatment position variation is used to refer to the variations inposition, orientation, and/or volumetric shape by which the currentstate of the target volume differs from the initial treatment alignment.By virtue of a known relationship between the treatment planningcoordinate system and the treatment room coordinate system, the termin-treatment alignment variation can also be used to refer to thevariations in position, orientation, or volumetric shape by which thecurrent state of the target volume differs from that in the treatmentplanning coordinate system. More generally, the term initial treatmentalignment or initial treatment position refers herein to the particularphysical pose or disposition (including position, orientation andvolumetric shape) of the body part of the patient upon patient setup atthe outset of the treatment fraction.

IGRT delivery system 104 comprises a radiation treatment (MV) source 108that selectively applies high-energy x-ray treatment radiation to atarget volume of a patient P positioned on a treatment couch TC. In onecommon scenario, the radiation treatment (MV) source 108 is a linearaccelerator (LINAC) producing therapeutic radiation (which can be termedan “MV source”). The radiation treatment source 108 applies thetreatment radiation under the control of a system controller 114, andmore particularly a treatment radiation control subsystem 128 thereof.System controller 114 may be a computing device that includes aprocessing device such as discussed above with reference to processingdevice 170. System controller 114 may also include a system memory and astorage device, similar to system memory 177 and storage device 180.System controller 114 further a detector controller 122, a couchposition controller 124, and an imaging device controller 126, eachprogrammed and configured to achieve one or more of the functionalitiesdescribed further herein. One or more imaging devices 110 selectivelyemit relatively low-energy (e.g., kV level) x-ray imaging radiationunder the control of imaging device controller 126, the imagingradiation being captured by one or more imaging detectors 112.

For one embodiment, the imaging devices 110 include a single x-rayimaging source. In other embodiments, the imaging devices 110 includepair of x-ray imaging sources usable to generate two-dimensionalstereotactic x-ray images. The imaging devices 110 may also include apair of x-ray imaging sources that are in fixed positions and a singlex-ray imaging source that is on a rotatable gantry. Preferably, each ofthe imaging devices 110 are characterized by either (a) a fixed,predetermined, nonmoving geometry relative to the (x, y, z) coordinatesystem of the treatment room, or (b) a precisely measurable and/orprecisely determinable geometry relative to the (x, y, z) coordinatesystem of the treatment room in the event they are dynamically moveable.The radiation treatment source 108 should also have a preciselymeasurable and/or precisely determinable geometry relative to the (x, y,z) coordinate system of the treatment room.

An imaging system of the IGRT delivery system 104 comprises one or moreindependent imaging devices 110 that produce relatively low intensitylower energy imaging radiation (each of which can be termed a “kVsource”). In-treatment images can comprise multiple two-dimensionalimages (typically x-ray images) acquired at multiple different points ofview (e.g., e.g., from multiple different angles), and may be comparedwith two-dimensional DRRs derived from the three-dimensionalpre-treatment image information (e.g., from a CT scan or MRI scan). ADRR is a synthetic x-ray image generated by casting hypothetical x-raysthrough the 3D imaging data, where the direction and orientation of thehypothetical x-rays simulate the geometry of the in-treatment x-rayimaging system. The resulting DRR then has approximately the same scaleand point of view as the in-treatment x-ray imaging system, and can becompared with the in-treatment x-ray images to determine the positionand orientation of the target, which is then used to guide delivery ofradiation to the target.

Target or target volume tracking during treatment may be accomplished bycomparing in-treatment tracking images to pre-treatment imageinformation. Pre-treatment image information may comprise, for example,computed tomography (CT) data, cone-beam CT data, magnetic resonanceimaging (MRI) data, positron emission tomography (PET) data or 3Drotational angiography (3DRA) data, and any information obtained fromthese imaging modalities (for example and without limitation digitallyreconstructed radiographs or DRRs).

A couch positioner 130 is actuated by the couch position controller 124to position the treatment couch TC. A non-x-ray based position sensingsystem 134 may sense position and/or movement of external marker(s)strategically affixed to the patient, and/or sense position and/ormovement of the patient skin surface itself, using one or more methodsthat do not involve ionizing radiation, such as optically based orultrasonically based methods.

In one embodiment, IGRT delivery system 104 is a gantry based IGRTdelivery system. In another embodiment, the IGRT delivery system 104 isa robotic arm based IGRT delivery system. IGRT delivery system 104further includes an operator workstation 116.

A non x-ray based position sensing system 134 may also be provided. Thisnon x-ray based position sensing system 134 may include, by way ofexample and without limitation, external markers affixed in some mannerto a patient's chest which move in response to respiration (othermechanisms for monitoring respiration may be used), and include a monoor stereoscopic x-ray imaging system, which as described above canprecisely determine target location. System 134 correlates motion of theexternal markers with target motion, as determined from (for example)the mono or stereoscopic tracking images generated by imaging device110. Non x-ray based position sensing system 134, therefore, permitssystem controller 114 to monitor external marker motion, use thecorrelation model to precisely predict where the target will be locatedin real time (e.g., ˜60 Hz), and direct the treatment beam to thetarget. As treatment of the moving target progresses additional x-rayimages may be obtained and used to verify and update the correlationmodel.

According to one embodiment, system controller 114, including processingdevice 120, is configured and programmed to receive information from thenon-x-ray based position sensing system 134 and/or the imagingdetector(s) 112 when treating a relatively stationary target volume (forexample and without limitation a brain, spine or prostate tumor),compute an in-treatment alignment variation therefrom, and control thetreatment radiation source 108 in a manner that compensates for thein-treatment alignment variation on a continual basis. In the case wherethe target volume moves due to respiration, the more information-richdata from imaging detectors 112 (e.g., x-ray-based data) is updated at arelatively slow rate compared to the breathing cycle of the patient (forexample, once every 15 seconds) to maintain reasonably low x-ray imagingdose levels. The less information-rich data from the non-x-ray basedposition sensing system 134 can be updated in substantially real-time(for example, 30 times per second). A correlation model between one ormore x-ray-sensed internal target volume (with our without fiducials)and one or more non-x-ray-sensed external markers may be used toascertain the in-treatment alignment variations on a real-time basis.The correlation model may be updated (corrected) at each x-ray imaginginterval to maintain accuracy. Advantageously, judicious imaging device110 angle selection strategies according to one or more of theembodiments described herein can be used to improve target trackingaccuracy.

It is to be appreciated that the use of a non-x-ray based positionsensing system 134 such as the SYNCHRONY® respiratory tracking systemrepresents an option that, while advantageous in the radiation treatmentof certain tumors within the lung or chest area, is not required forradiation treatments in many other body parts, such as the prostate,spine or brain. Whereas x-ray dosage concerns provide limits on thenumber of kV x-ray images that should be acquired in any particularintrafraction time interval (for example, no more than one kV imageevery 15 seconds, every 30 seconds, or every 60 seconds), tumors withinthe chest area, liver or pancreas can move at substantially fasterperiodic rates due to respiration, therefore giving rise to theusefulness of the non-x-ray based position sensing system 134. However,tumors in other parts of the body, such as the prostate, spine or brain,will generally experience motion on a much slower time scale, whereinthe dose-limited kV x-ray imaging rate will be still be sufficientlyhigh to effectively guide the radiation treatment. The prostate, forexample, may experience quasi-static movement due to an accumulation ofurine in the nearby urinary bladder, an event for which one kV x-rayimage every 60 seconds may be sufficient to track resultant movement.Accordingly, for the many other parts of the anatomy for which kVimaging rates are sufficient, the non-x-ray based position sensingsystem 134 and the associated “real time” tracking (i.e., tracking at arate faster than the kV imaging rate) may not be used.

It is to be appreciated that the exemplary radiation treatmentenvironment of FIG. 1 is presented by way of example and not by way oflimitation. Embodiments are applicable in a variety of other radiationtreatment environment configurations, and one or more of the embodimentsis applicable to general medical imaging environments outside theparticular context of radiation treatment systems. Thus, for example,while one or more of the embodiments is particularly advantageous whenapplied in the context of a radiation treatment environment in which thereference imaging system 102 is physically separated from, has no commoncoordinate system with, and/or has no other intrinsic means ofvolumetric image registration with the IGRT delivery system 104, thescope of the present teachings is not so limited. Rather, embodimentscan also be advantageously applied in the context of radiation treatmentenvironments in which the reference imaging system is physicallyintegral with radiation treatment delivery system or has other intrinsiclinkages, such as a rail-based patient movement system, with theradiation treatment delivery system.

As used herein, “registration” of images refers to the determination ofa mathematical relationship between corresponding anatomical or other(e.g. fiducial) features appearing in those images. Registration caninclude, but is not limited to, the determination of one or more spatialtransformations that, when applied to one or both of the images, wouldcause an overlay of the corresponding anatomical features. The spatialtransformations can include rigid-body transformations and/or deformabletransformations and can, if the images are from different coordinatesystems or reference frames, account for differences in those coordinatesystems or reference frames. For cases in which the images are notacquired using the same imaging system and are not acquired at the sametime, the registration process can include, but is not limited to, thedetermination of a first transformation that accounts for differencesbetween the imaging modalities, imaging geometries, and/or frames ofreference of the different imaging systems, together with thedetermination of a second transformation that accounts for underlyinganatomical differences in the body part that may have taken place (e.g.,positioning differences, overall movement, relative movement betweendifferent structures within the body part, overall deformations,localized deformations within the body part, and so forth) betweenacquisition times.

In some embodiments at least one imaging device 110 is mounted to arotatable gantry. The treatment radiation source 108 may or may not bemounted to the rotatable gantry. The at least one imaging device 110 maygenerate tracking images of a target from multiple different angles. Inone embodiment, as discussed in greater detail below, treatment planningsystem 118 and/or IGRT delivery system 104 determines a set of anglesthat may be used to generate tracking images by the imaging device 110.As also discussed below, in embodiments IGRT delivery systems mayperform target tracking based on tracking images taken at one or moreangles from the determined set of angles.

FIGS. 2-6 are flow charts illustrating various methods of selectingangles for use during a treatment stage. The methods of FIGS. 2-6 may beperformed prior to treatment and may be referred to as pre-treatmentangle selection methods. The methods may be performed by a processinglogic that may comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructions runon a processing device to perform hardware simulation), or a combinationthereof. The methods of FIGS. 2-6 may be performed by processing logicof a treatment planning system (e.g., treatment planning system 118 ofFIG. 1) and/or by processing logic of an IGRT delivery system (e.g.,IGRT delivery system 104 of FIG. 1) in embodiments. After the angles areselected, an imaging device that is a component of an IGRT deliverysystem may use a subset of the selected angles to generate trackingimages and track a target during a treatment stage.

FIG. 2 illustrates a method 200 of selecting a set of angles for use bya rotational imaging device to be used during treatment, in accordancewith one embodiment of the present invention. Method 200 may begin bygenerating a three-dimensional treatment planning image of a patient atblock 205. In one embodiment, as illustrated, the three-dimensionaltreatment planning image is a computer tomography (CT) scan of thepatient. The CT scan may be a three-dimensional (3D) CT scan or afour-dimensional (4D) CT scan. Other three-dimensional treatmentplanning images or 4D treatment planning images using other imagemodalities may also be used. Method 200 will be discussed with referenceto a CT scan. However, it should be understood that method 200 may beperformed using any other three-dimensional or four-dimensionaltreatment planning images. For example, at block 205 an MRI image may begenerated instead of a CT scan image. The MRI image may be a 3D MRIimage (also referred to as a 3D MRI scan) or a 4D MRI image (alsoreferred to as a 4D MRI scan).

Once the CT scan is generated, a physician and/or technician maydelineate a target within the CT scan at block 210. This may includedelineating the target in multiple different slices of the CT scan.After the target has been delineated, the target location and shape isknown in 3D space in the CT scan. In addition, additional structures mayalso be delineated, such as a spine, a heart, a liver, a humerus, amediastinum, and so on. The additional structures that are delineatedmay be dense structures in the patient. The operations of blocks 205 and210 may have already been performed in some embodiments, and may not bea part of method 200. In such embodiments, method 200 may begin byreceiving a pre-generated CT scan in which the target and/or additionalstructures have already been delineated.

At block 215, processing logic determines a plurality of angles fromwhich tracking images can be generated by an imaging device (e.g., byimaging device 110 of FIG. 1). The imaging device may be mounted to arotatable gantry that may rotate 360 degrees about an axis. Images maybe taken from any of the possible angles of the imaging device. In someembodiments, a radiation treatment source is also mounted to therotatable gantry. Depending on a mode of operation, the rotatable gantrymay continuously rotate during a treatment stage, and may generatetracking images from any of the possible angles while rotating.Alternatively, the rotatable gantry may rotate to specific angles andstop at those angles to take tracking images during a treatment stage.

At block 218, processing logic analyzes each of the determined angles.Analysis of the angles may include generating, at block 220, a pluralityof projections of the CT scan of the patient. Each of the projections isgenerated for a different angle at which the imaging device may bepositioned. In one embodiment, 360 projections are generated for angles1 degree through 360 degrees. Thus, the projections may be generated forevery 1 degree of angle separation. Alternatively, projections may begenerated, for example, at every 5 degrees of angle separation (e.g., at5 degrees, 10 degrees, 15 degrees, and so on), at every 10 degrees ofangle separation, at every 0.5 degree of angle separate, and so on.Multiple different types of projections may be generated, as discussedbelow with reference to FIGS. 3A-3D. Some examples of projections thatmay be generated include digitally reconstructed radiographs (DRRs),geometric projections, ray traces of one or more rays, and so on. A DRRis a virtual x-ray image that is generated from a 3D CT image based onsimulating the x-ray image formation process by casting rays through theCT image. Any of the projections may be projected onto a virtualdetector plane.

Analysis of the angles may further include, at block 225, analyzing theprojections generated at the various angles. The analysis that isperformed may be dependent on the type of projection that was generatedand/or the tracking quality metric criteria to be applied. Some examplesof the different analyses that may be performed are described below withreference to FIGS. 3A-6. Based on the analysis, processing logicdetermines tracking quality metric values for tracking the target ateach of the angles for which projections were generated. A trackingquality metric value for an angle represents a confidence that a targettracking algorithm will be able to successfully track the target basedon images generated at that angle. In other words, the tracking qualitymetric value is a value (e.g., a number) that is a proxy for a trackingsuccess probability that enables angles to be ranked and optimal anglesto be selected. In one embodiment, a higher tracking quality metricvalue indicates a higher confidence that the target can be tracked froman angle and a lower tracking quality metric value indicates a lowerconfidence that the target can be tracked from an angle. Numerousdifferent inputs may be used to compute the tracking quality metricvalue for an angle. These inputs may be used individually or incombination to compute the tracking quality metric value. Where multipleinputs are used, the inputs may or may not be weighted. Examples ofdifferent tracking quality metric values (and inputs for trackingquality metric values) are discussed with reference to FIGS. 3A-6 below.

At block 228, processing logic selects a subset of the angles for whichprojections were generated. Angles may be selected for inclusion in thesubset based on the tracking quality metric values associated with thoseangles. The angles that are selected for inclusion in the subset have atracking quality metric value that satisfies a tracking quality metriccriterion (or multiple tracking quality metric criteria). In oneembodiment, the tracking quality metric criteria include a trackingquality metric threshold. Those angles associated with tracking qualitymetric values that meet or exceed the tracking quality metric thresholdmay be included in the subset, while those angles associated withtracking quality metric values below the threshold may not be includedin the subset. The tracking quality metric threshold may be a fixedthreshold or a variable threshold. For a variable threshold, thethreshold may be determined based on the computed tracking qualitymetric values for a particular patient. For example, if the highesttracking quality metric value was 0.6, then the threshold may be 0.5.For a fixed threshold, the threshold may be determined withoutconsideration of the computed tracking quality metric values for aparticular patient. In some instances that may lead to there being noangles that satisfy the tracking quality metric criteria.

At block 230, processing logic may order the angles based on theirassociated tracking quality metric values. The subset of angles to beused for tracking purposes may be those angles having highest trackingquality metric values. Accordingly, optimal angles may be determined forthe purpose of generating images to track a target during a treatmentstage of a patient.

In some instances, processing logic may determine that there is aninsufficient number of angles in the subset that have the trackingquality metric value that satisfies the one or more tracking qualitymetric criteria. One possible cause for an insufficient number ofavailable angles is a large distance between the target and a treatmentisocenter. Accordingly, if there is an insufficient number of angles inthe subset, processing logic may output a suggestion that the patient berepositioned to cause the target to be closer to the treatmentisocenter. For example, processing logic may recommend that the patientbe repositioned 4 cm to the left. Patient repositioning may be performedby physically repositioning the patient on a treatment couch or byautomatically moving the treatment couch vertically and/or laterally.After the repositioning, method 200 may be repeated to determine a newsubset of the plurality of angles.

FIGS. 3A-3D illustrate various methods for computing tracking qualitymetric values for possible angles of an imaging device. Any one of thesemethods may be performed at block 218 of method 200 to determine qualitymetric values for the different angles. Additionally or alternatively,two of more of these methods may be combined to determine multiplequality metric values for each angle or to determine a single combinedquality metric value based on the different methods. If multiple methodsare used, then the quality metric values output by each of these methodsmay be weighted equally or unequally to compute a final combined qualitymetric value.

FIG. 3A illustrates a first method 300 of determining tracking qualitymetric values for angles of a rotational imaging device, in accordancewith one embodiment of the present invention. At block 302 of method 300processing logic selects an angle. The selected angle corresponds to anangle of an imaging device that can be used to generate tracking images.At block 304, processing logic generates a target region DRR for theselected angle. A target region DRR is a DRR that is generated bycasting or tracing rays through just the target in a CT scan or othertreatment planning image (e.g., an MRI image). DRR pixel values may becomputed by summing CT values along each ray. At block 306, a standardDRR is generated for the selected angle. T The standard DRR differs fromthe target region DRR in that the rays are traced through all regions ofthe CT scan (or other treatment planning image) to generate the standardDRR. For both the standard DRR and the target region DRR the rays may betraced onto a virtual detector plane. Each pixel of the virtual detectorplane may correspond to a ray traced through the CT scan data (or othertreatment planning image data). The pixel value for a pixel may be basedon an aggregation of the CT values of the associated ray.

At block 308, processing logic uses a target tracking algorithm tosearch for the target in the standard DRR using the target region DRR.The target tracking algorithm may be the same target tracking algorithmthat will be used to track the target during a treatment stage fortreatment of the patient. However, during treatment the target trackingalgorithm may find the target in tracking images such as x-ray images.

In one embodiment, the target tracking algorithm performs patternmatching based on similarity values between the target region DRR andthe standard DRR, as described in the example below and indicated inblock 310. In such an example, the target tracking algorithm determinescharacteristics or patterns such as a shape of the target from thetarget region DRR. The target tracking algorithm computes similarityvalues between a first pattern of the target from the target region DRRand patterns for each of several candidate locations for the target inthe standard DRR. The maximum of similarity values between the firstpattern from the target region DRR and the additional patterns from thestandard DRR indicates a location of the target in the standard DRR. Thetracking quality metric value may be proportional to the degree ofsimilarity between the first pattern and the closest pattern from thestandard DRR in some embodiments.

A similarity value for a candidate location may be based on acombination of similarity values for the target region DRR at thecandidate location. “Similarity values” or “similarity measures” arenumbers that reflect the degree to which two images are similar to eachother. For example, a cross-correlation or combination of severalcross-correlations between two images can be used to compute asimilarity value. One embodiment for locating a target proceeds byassembling a similarity map over a tracking region for the target regionDRR. The similarity map contains a similarity value for the DRR at eachof the candidate locations considered in the image. Similarity values asdescribed above may be computed using, but not limited to,cross-correlation, entropy, mutual information, gradient correlation,pattern intensity, gradient difference, or image intensity gradientsmethods. The computed values may be normalized so that the resultingsimilarity value is a number ranging between 0 and 1 or −1 and 1. Thehighest similarity value may be used to generate a tracking qualitymetric value for the selected angle.

The target may not be located in the standard DRR based on the targetregion DRR in some instances. For example, the target may not be locatedif the target is occluded by a bone structure or other dense structuresuch as the heart or diaphragm. Failure to locate the target may resultin a tracking quality metric value that fails to satisfy a trackingquality metric criterion.

In some embodiments, the tracking quality metric value is a confidencevalue. The tracking quality metric value may be based on the highestsimilarity value for a candidate location. The tracking quality metricvalue may also be based on a difference between the highest similarityvalue for a candidate location and other similarity values for othercandidate locations. If a difference between the highest similarityvalue and other similarity values is below a difference threshold, thisindicates that from the selected angle there are other structures in thepatient that resemble the target. Such similar structures may confusethe tracking algorithm during treatment and so cause the confidencevalue to be reduced. Additionally, the actual location of the target inthe standard DRR is known. If the location determined by the targettracking algorithm differs from the known location, then the trackingquality metric value may also be reduced.

At block 312, processing logic records the results of the targettracking algorithm. This may be a single tracking quality metric valuesuch as a confidence value. The results of the target tracking algorithmmay alternatively include multiple values, such as a binary success/failvalue and a confidence value.

At block 314, processing logic determines if there are any additionalangles for which tracking quality metric values still need to bedetermined. If so, the method returns to block 302 and a new angle isselected. Otherwise the method ends.

FIG. 3B illustrates a second method 320 of determining tracking qualitymetric values for angles of a rotational imaging device, in accordancewith one embodiment of the present invention. At block 322 of method 320processing logic selects an angle. The selected angle corresponds to anangle of an imaging device that can be used to generate tracking images.At block 324, processing logic generates a DRR (e.g., a standard DRR)for the selected angle. At block 325, processing logic computes one ormore quality metric values for the selected angle based on the DRR.Multiple different techniques may be used to compute the quality metricvalues, some of which are described herein. However, this disclosure isnot limited only to those techniques described herein for computing thetracking quality metric values. If multiple quality metric values aredetermined, these values may be combined into a combined quality metricvalue. The combined quality metric value may be based on a weighted ornon-weighted combination of the different quality metric values.

In one embodiment, at block 326 processing logic determines a contrastbetween the target and a region surrounding the target. The location ofthe target is known since the target is delineated in the CT scan (orother treatment planning image) used to generate the DRR. The contrastmay be a difference in intensities between the target and the background(surrounding region). Accordingly, a contrast between a first regioninside of the target and a second region outside of the target may becomputed without a need to first locate the target.

There are multiple different contrast values that may be used alone orin combination to compute the tracking quality metric value (or an inputfor a tracking quality metric value). One contrast value that may bedetermined is the luminance contrast, which is the difference inintensities between the target and its background (the surroundingregion) divided by an intensity of the background. Another contrastvalue that may be computed is a contrast to noise ratio (CNR). CNR iscomputed by dividing the luminance contrast by a standard deviation ofoverall image noise. Noisy images generally require a larger contrast tooffer similar visibility of the target. Other types of contrast that maybe computed include Weber contrast, Michelson contrast and root meansquare (RMS) contrast.

Higher contrast values indicate an increased probability of finding thetarget during treatment. Accordingly, higher contrast values arepreferable. In one embodiment, tracking quality metric criteria includea minimum acceptable contrast and/or a minimum acceptable contrast tonoise ratio. The minimum acceptable contrast may be determined based ona combination (e.g., an average) of the contrasts computed for DRRs atmultiple different angles. In one embodiment, an angle having a contrastvalue that is below the minimum acceptable contrast (and/or below theminimum acceptable contrast to noise ratio) fails to satisfy the one ormore tracking quality metric criteria.

In one embodiment, at block 328 processing logic determines an edge ofthe target. The edge of the target may be determined easily because thelocation of the target is delineated in the CT scan (or other treatmentplanning image) and so is known. At block 330, processing logicdetermines an edge strength for the edge of the target. The edgestrength may be determined by computing changes in image brightnessand/or other image properties at the edge. In one embodiment, a firstorder derivative of the change in image brightness at the edge iscomputed. Other mathematical techniques may also be used to compute theedge strength. A higher edge strength indicates a higher likelihood offinding the target during a treatment stage. In one embodiment, trackingquality metric criteria include a minimum acceptable edge strength. Inone embodiment, an angle having an edge strength value that is below theminimum acceptable edge strength fails to satisfy the tracking qualitymetric criteria.

As mentioned previously, the CT scan (or other treatment planning image)from which the DRR is generated at block 324 includes a delineatedtarget and may also include one or more additional delineatedstructures, such as a spine, heart, diaphragm, and so on. In oneembodiment, at block 332 processing logic determines whether there isoverlap between the target and an additional delineated structure in theDRR. Overlap between delineated structures may indicate that the targetor a portion of the target may not be visible in tracking images takenfrom the angle, and may cause a target tracking algorithm used duringtreatment to fail to find the target. In one embodiment, trackingquality metric criteria include a maximum acceptable overlap between thetarget and additional delineated structures. In one embodiment, trackingquality metric criteria include a minimum acceptable distance betweenthe target and additional delineated structures. In one embodiment, anoverlap between the delineated target and an additional delineatedstructure causes the tracking quality metric value for the angle to failto satisfy the tracking quality metric criteria.

At block 334, processing logic records the results of the one or morequality metric values. Each of these tracking quality metric values maybe inputs for a combined tracking quality metric value. At block 336,processing logic determines if there are any additional angles for whichtracking quality metric values still have not been determined. If so,the method returns to block 322 and a new angle is selected. Otherwisethe method ends.

FIG. 3C illustrates a third method 340 of determining tracking qualitymetric values for angles of a rotational imaging device, in accordancewith one embodiment of the present invention. At block 342 of method 340processing logic selects an angle. The selected angle corresponds to anangle of an imaging device that can be used to generate tracking images.At block 344, processing logic traces a ray through the target in the CTscan or other treatment planning image at the selected angle. In oneembodiment, the ray goes through a centroid of the target. At block 346,processing logic accumulates CT values for the ray as the ray traversesthe CT scan.

At block 348, processing logic generates an effective depth value basedon an accumulation of the CT values or values of another 3D or 4Dtreatment planning image. The effective depth value represents a totalaccumulated density of material traversed by the ray. Higher effectivedepth values indicate that the ray passed through dense material,whereas lower effective depth values indicate that the ray passedthrough less dense material. Accordingly, lower effective depth valuesare preferred in embodiments. The effective depth value for the ray maybe a tracking quality metric value for the angle. Alternatively, theeffective depth value for the ray may be one input for a trackingquality metric value. In one embodiment, the tracking quality metriccriteria include a maximum acceptable effective depth value. In oneembodiment, an effective depth value higher than the maximum acceptableeffective depth value causes the tracking quality metric value to failto satisfy the one or more tracking quality metric criteria.

In some embodiments multiple rays are traced through the target at theselected angle, and effective depth values may be determined for eachray. For example, ray tracing may be performed for anywhere from tworays that pass through the target to all rays that pass through thetarget. The effective depth values may then be mathematically combinedto determine a combined effective depth value. In one embodiment, theeffective depth values of the multiple rays are averaged to compute anaverage effective depth value. In one embodiment, a median effectivedepth value is computed. The combined effective depth value, averageeffective depth value and/or median effective depth value may be used asthe tracking quality metric value or as an input to the tracking qualitymetric value. The effective depth value (or values) and/or the trackingquality metric value may then be recorded for the angle.

At block 350, processing logic determines if there are any additionalangles for which tracking quality metric values still need to bedetermined. If so, the method returns to block 342 and a new angle isselected. Otherwise the method ends.

FIG. 3D illustrates a fourth method 360 of determining tracking qualitymetric values for angles of a rotational imaging device, in accordancewith one embodiment of the present invention. Method 360 may be used todetermine the tracking quality metric values of tracking a target basedon tracking fiducials that are implanted in the target. The target mayhave multiple fiducials that have been implanted in the target. Trackingof the target in three-dimensional space may be optimal at angles whereeach of the fiducials is separately viewable in images taken at thoseangles.

At block 362 of method 360 processing logic selects an angle. Theselected angle corresponds to an angle of an imaging device that can beused to generate tracking images. At block 364, processing logicprojects positions of the fiducials from a 3D space of the CT scan (orother treatment planning image) at the selected angle onto a 2D virtualdetector plane. The projection may be a geometric projection of thefiducials.

At block 366, processing logic determines whether any of the fiducialsoverlaps with any other fiducial in the target. In one embodiment, afiducial overlap tracking quality metric criterion is a binary criterionbased on whether or not there is overlap between any two fiducials. Ifthere is overlap between fiducials, the fiducial overlap trackingquality metric criterion may not be satisfied for an angle. In oneembodiment, a tracking quality metric criterion may be a numeric valuebased on an allowed amount of overlap between fiducials. Greater overlapbetween fiducials may result in poorer tracking results, and thus mayresult in a lower tracking quality metric value.

At block 368, processing logic determines an amount of separationbetween the fiducials. An amount of separation may be determined betweeneach of the fiducials. For example, if there are three fiducials, then aseparation value may be determined between the first and secondfiducials, between the second and third fiducials, and between the firstand third fiducials. Alternatively, separation values may be computedbetween closest fiducials in the geometric projection. A higher amountof separation between fiducials may result in better tracking results,and so may be preferred in embodiments. The minimum separation betweenfiducials for an angle may be used as a tracking quality metric valuefor that angle. Alternatively, a tracking quality metric value may becomputed at least in part based on the minimum separation betweenfiducials, the average separation, the amount of overlap, and so on.

At block 369, processing logic records the results of the one or morequality metric values. At block 370, processing logic determines ifthere are any additional angles for which tracking quality metric valuesare still to be determined. If so, the method returns to block 364 and anew angle is selected. Otherwise the method ends.

FIG. 4 illustrates a method 400 of selecting a set of angles for use bya rotational imaging device to be used during treatment, in accordancewith one embodiment of the present invention. In certain embodimentsmethod 400 is substantially similar to method 200. Specifically,embodiments of method 400 correspond to performing the operations ofmethod 200 for multiple different couch positions of a movable treatmentcouch and selecting a subset of angles based on combined results of thetracking quality metric values for the angles at the multiple couchpositions.

At block 415, processing logic determines a plurality of angles fromwhich tracking images can be generated by an imaging device (e.g., byimaging device 110 of FIG. 1). The imaging device may be mounted to arotatable gantry that may rotate 360 degrees about an axis. Images maybe taken from any of the possible angles of the imaging device.

At block 418, processing logic analyses the plurality of angles for afirst couch position of the treatment couch. At block 420, analysis ofthe angles includes generating a first plurality of projections of thetreatment planning image of the patient for the first couch position.Each of the projections is generated for a different angle at which theimaging device may be positioned. In one embodiment, 360 projections aregenerated for angles 1 degree through 360 degrees. Thus, the projectionsmay be generated for every 1 degree angle separation. Alternatively,projections may be generated, for example, at every 5 degree angleseparation (e.g., at 5 degrees, 10 degrees, 15 degrees, and so on), atevery 10 degree angle separation, at every 0.5 degree angle separate,and so on. Multiple different types of projections may be generated, asdiscussed above with reference to FIGS. 3A-3D. Some examples ofprojections that may be generated include digitally reconstructedradiographs (DRRs), geometric projections, ray traces of one or morerays, and so on.

Analysis of the angles may further include, at block 425, analyzing theprojections generated at the various angles for the first couchposition. The analysis that is performed may be dependent on the type ofprojection that was generated. Examples of the different analyses thatmay be performed are described above with reference to FIGS. 3A-3D.Based on the analysis, processing logic determines tracking qualitymetric values for tracking the target for the first couch position andat each of the angles for which projections were generated.

At block 428, processing logic analyses the plurality of angles for asecond couch position of the treatment couch. In one embodiment, thefirst couch position and the second couch position represent twoopposite extremes of couch positions that may be used during a treatmentstage for the patient.

At block 430, analysis of the angles includes generating a secondplurality of projections of the treatment planning image of the patientfor the second couch position. Analysis of the angles may furtherinclude, at block 435, analyzing the projections generated at thevarious angles for the second couch position. The analysis that isperformed may be dependent on the type of projection that was generated.Examples of the different analyses that may be performed are describedabove with reference to FIGS. 3A-3D. Based on the analysis, processinglogic determines tracking quality metric values for tracking the targetfor the second couch position and at each of the angles for whichprojections were generated.

In some embodiments, additional analyses of the plurality of angles mayalso be performed for additional couch positions. For each such couchposition different tracking quality metric values may be determined ateach of the plurality of angles.

At block 440, processing logic selects a subset of the angles for whichprojections were generated. Angles may be selected for inclusion in thesubset based on the quality metric values associated with those anglesat the multiple different couch positions. The angles that are selectedfor inclusion in the subset have a tracking quality metric value thatsatisfies a tracking quality metric criterion (or multiple trackingquality metric criteria) at each of the different couch positions thatare considered. In one embodiment, the tracking quality metric criteriainclude a tracking quality metric threshold. Those angles associatedwith tracking quality metric values that meet or exceed the trackingquality metric threshold may be included in the subset, while thoseangles associated with tracking quality metric values below thethreshold may not be included in the subset. The tracking quality metricthreshold may be a fixed threshold or a variable threshold.

Processing logic may then order the angles based on their associatedtracking quality metric values. The subset of angles to be used fortracking purposes may be those angles having highest tracking qualitymetric values at multiple different couch positions. Accordingly,optimal angles may be determined for the purpose of generating images totrack a target during a treatment stage of a patient at a range ofpossible couch positions that might be used during the treatment stage.

FIG. 5 illustrates a method 500 of selecting a set of angles for use bya rotational imaging device to be used during treatment, in accordancewith one embodiment of the present invention. In certain embodimentsmethod 500 is substantially similar to method 200. Specifically,embodiments of method 500 correspond to performing the operations ofmethod 200 for multiple different times of a 4D CT scan (or other 4Dtreatment planning image) and selecting a subset of angles based oncombined results of the tracking quality metric values for the angles atthe multiple different time slices.

At block 515, processing logic determines a plurality of angles fromwhich tracking images can be generated by an imaging device (e.g., byimaging device 110 of FIG. 1). The imaging device may be mounted to arotatable gantry that may rotate 360 degrees about an axis. Images maybe taken from any of the possible angles of the imaging device.

At block 518, processing logic analyses the plurality of angles for afirst time of the 4D CT scan (or other 4D treatment planning image). Atblock 420, analysis of the angles includes generating a first pluralityof projections of the CT scan (or other 3D treatment planning image) ofthe patient for the first time. Each of the projections is generated fora different angle at which the imaging device may be positioned. In oneembodiment, 360 projections are generated for angles 1 degree through360 degrees. Thus, the projections may be generated for every 1 degreeangle separation. Alternatively, projections may be generated, forexample, at every 5 degree angle separation (e.g., at 5 degrees, 10degrees, 15 degrees, and so on), at every 10 degree angle separation, atevery 0.5 degree angle separate, and so on. Multiple different types ofprojections may be generated, as discussed above with reference to FIGS.3A-3D. Some examples of projections that may be generated includedigitally reconstructed radiographs (DRRs), geometric projections, raytraces of one or more rays, and so on. A DRR is a virtual x-ray imagethat is generated from a 3D CT image (or other 3D treatment planningimage) based on simulating the x-ray image formation process by castingrays through the CT image (or other 3D treatment planning image). Any ofthe projections may be projected onto a virtual detector plane.

Analysis of the angles may further include, at block 525, analyzing theprojections generated at the various angles for the first time. Theanalysis that is performed may be dependent on the type of projectionthat was generated. Examples of the different analyses that may beperformed are described above with reference to FIGS. 3A-3D. Based onthe analysis, processing logic determines tracking quality metric valuesfor tracking the target for the first time and at each of the angles forwhich projections were generated.

At block 528, processing logic analyzes the plurality of angles for asecond time of the 4D CT scan (or other 4D treatment planning image). Inone embodiment, the first time and the second time represent twoopposite extremes of position and/or rotation that the target mayachieve during a treatment stage for the patient. The target may undergomotion during the treatment stage. The 4D CT scan may capture motion ofthe target over a time period, and that captured motion may correspondto a motion that the target is likely to also undergo during thetreatment stage. Accordingly, it can be beneficial to identify andselect angles that will be optimal throughout the motion of the target.Some types of motion that a target may undergo are cyclical motions. Forexample, targets that are located in the lung region of a patient maymove, change shape and/or rotate with inhalation and exhalation of thepatient.

At block 530, analysis of the angles includes generating a secondplurality of projections of the CT scan (or other treatment planningimage) of the patient for the second time of the 4D CT scan (or other 4Dtreatment planning image). Analysis of the angles may further include,at block 535, analyzing the projections generated at the various anglesfor the second time of the 4D CT scan (or other 4D treatment planningimage). The analysis that is performed may be dependent on the type ofprojection that was generated. Examples of the different analyses thatmay be performed are described above with reference to FIGS. 3A-3D.Based on the analysis, processing logic determines tracking qualitymetric values for tracking the target for the second time and at each ofthe angles for which projections were generated.

In some embodiments, additional analyses of the plurality of angles mayalso be performed for additional times of the 4D CT scan or other 4Dtreatment planning image. For each such time slice different trackingquality metric values may be determined at each of the plurality ofangles.

At block 540, processing logic selects a subset of the angles for whichprojections were generated. Angles may be selected for inclusion in thesubset based on the quality metric values associated with those anglesat the multiple different times of the 4D CT scan (or other 4D treatmentplanning image). The angles that are selected for inclusion in thesubset have a tracking quality metric value that satisfies a trackingquality metric criterion (or multiple tracking quality metric criteria)at each of the times that are considered. In one embodiment, thetracking quality metric criteria include a tracking quality metricthreshold. Those angles associated with tracking quality metric valuesthat meet or exceed the tracking quality metric threshold may beincluded in the subset, while those angles associated with trackingquality metric values below the threshold may not be included in thesubset. The tracking quality metric threshold may be a fixed thresholdor a variable threshold.

Processing logic may then order the angles based on their associatedtracking quality metric values. The subset of angles to be used fortracking purposes may be those angles having highest tracking qualitymetric values at multiple times of the 4D CT scan (or other 4D treatmentplanning image). Accordingly, optimal angles may be determined for thepurpose of generating images to track a target at different stages of acyclic motion that the target may undergo during a treatment stage.

FIG. 6A illustrates a method 600 of selecting a set of angles for use bya rotational imaging device to be used during treatment of a movingtarget, in accordance with one embodiment of the present invention. Inone embodiment, method 600 is performed after block 228 of method 200.

At block 605 of method 600, processing logic generates a motion model ofthe target. The motion model may be for a cyclic motion (e.g., a motionthat occurs with breathing) or for a quasi-static motion. A quasi-staticmotion is a motion that occurs slowly in a predictable episodic manner.One example of a target that may undergo a quasi-static motion is aprostate. The motion model may be generated based on statisticalinformation about how targets of a particular type generally move. Forexample, a statistical motion model may be generated for prostate motionbased on statistical information about how prostates of many patientshave been detected to move. The motion model may alternatively oradditionally be based on additional information, such as on a 4D CT scanor 4D magnetic resonance imaging (MRI) image of the patient showingmotion of the target.

At block 610, processing logic estimates a future motion of the targetfrom the motion model. At block 615, processing logic determines anestimated future position and orientation (and possibly size and/orshape) of the target based on applying the estimated future motion tothe target in the CT scan or other treatment planning image. At block620, processing logic determines, for each angle of the plurality ofangles, a second tracking quality metric value for tracking the targethaving the estimated future position and orientation. At block 630,processing logic determines a second subset of the subset of angleshaving tracking quality metric values that satisfy the one or moretracking quality criteria. The second subset may be a further subsetthat may include less than all of the angles in the subset. The secondsubset includes angles with a second tracking quality metric value thatalso satisfies the tracking quality metric criteria.

FIG. 6B illustrates a method 650 of determining tracking quality metricvalues for angles based on a motion model, in accordance with oneembodiment of the present invention. Method 650 may be performed, forexample, at block 218 of method 200.

At block 655 of method 650, processing logic generates a motion model ofa target. The motion model may be for a cyclic motion (e.g., a motionthat occurs with breathing) or for a quasi-static motion. The motionmodel may be generated based on statistical information about howtargets of a particular type generally move. For example, a statisticalmotion model may be generated for prostate motion based on statisticalinformation about how prostates of many patients have been detected tomove. The motion model may alternatively or additionally be based onadditional information, such as on a 4D CT scan or other 4D treatmentplanning image of the patient showing motion of the target.

At block 658, processing logic may determine tracking quality metricvalues for the plurality of angles based on position extremes of thetarget. In one embodiment, at block 660 processing logic estimates afirst position and a second position of the target from the motion modelfor each angle of the plurality of angles. The first position and thesecond position may be at two extremes of the motion model. For example,if the motion model is for motion of a target in the lungs, a firstposition may be at full inhalation and a second position may be at fullexhalation.

In one embodiment, at block 665 processing logic determines, for eachangle of the plurality of angles, a first tracking quality metric valuefor the first position and a second tracking quality metric value forthe second position. At block 670 processing logic may then determine,for each angle of the plurality of angles, a combined tracking qualitymetric value based on the first tracking quality metric value for thefirst position and the second tracking quality metric value for thesecond position. In one embodiment, the combined tracking quality metricvalue is an average of the two tracking quality metric values. In oneembodiment, the combined tracking quality metric value is maintained astwo separate tracking quality metric values, and each of these trackingquality metric values is separately compared against the trackingquality metric criteria.

In one embodiment, at block 680 processing logic determines trackingquality metric values based on motion sensitivity at each of theplurality of angles. In one embodiment, at block 685 processing logicdetermines a sensitivity to target motion for each angle of theplurality of angles. Determining the motion sensitivity for an angle mayinclude determining an amount of motion that is detectable at thatangle. For example, processing logic may determine a principal axis ofmotion for the target. Those angles that are approximately orthogonal tothe principal axis of motion may detect the largest amount of motion forthe target. For example, motion of a target that has significantanterior-posterior motion may be most visible in images generated fromleft and right imaging angles rather than anterior and posterior imagingangles.

At block 690 processing logic may then determine, for each angle of theplurality of angles, a tracking quality metric value based on thesensitivity to the target motion at that angle (e.g., based on theamount of motion that is detectable at that angle). A higher motionsensitivity (higher amount of detectable motion) may indicate thattarget motion will more likely be captured from an angle duringtreatment, thus increasing target tracking accuracy. Accordingly, highermotion sensitivity may result in a higher tracking quality metric value.

Numerous different tracking quality metric values and inputs fortracking quality metric values have been discussed herein with referenceto FIGS. 2-6B. However, it should be understood that embodiments are notlimited to those tracking quality metric values and inputs describedherein. These tracking quality metric values and/or inputs for trackingquality metric values may be combined together and/or with other inputsand/or metrics in any combination. For example, input for a trackingquality metric value, which may be used alone or in conjunction with anyof the other tracking quality metric value inputs discussed herein, maybe based on a field of view (FOV) for a projection associated with anangle. Due to a position of the target with respect to a treatmentisocenter, a FOV of a projection for different angles may vary. LargerFOVs provide greater information, and so may be preferable. Accordingly,one tracking quality metric criterion may be a FOV size threshold. Ifthe FOV of a projection associated with a particular angle is below theFOV size threshold, then the tracking quality metric value for thatangle may be reduced and/or the tracking quality metric value may failto satisfy the tracking quality metric criteria.

FIGS. 7-9C are flow charts illustrating various methods of selectingangles for use during an alignment phase and/or a treatment phase of atreatment stage. Accordingly, the methods of FIGS. 7-9C may be used forin-treatment angle selection in embodiments. The methods may beperformed by a processing logic that may comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device to performhardware simulation), or a combination thereof. The methods of FIGS.7-9C may be performed by processing logic of a treatment planning system(e.g., treatment planning system 118 of FIG. 1) and/or by processinglogic of an IGRT delivery system (e.g., IGRT delivery system 104 ofFIG. 1) in embodiments.

The methods of FIGS. 7-9C may be performed, for example, by an IGRTdelivery system that includes a rotational imaging device that mayrotate about a treatment couch (and a patient) by 360 degrees (e.g., ina 360 degree arc). In some embodiments, the rotational imaging device ismounted to a rotatable gantry to which a radiation treatment source isalso mounted. In other embodiments, the rotational imaging device ismounted to a rotatable gantry or ring that does not include a radiationtreatment source mounted to it. In one embodiment, the IGRT deliverysystem is a gantry-based IGRT delivery system, such as shown in FIGS.10A-10C. The gantry-based IGRT delivery system of FIGS. 10A-10C is onetype of helical delivery radiation therapy apparatus. For helicaldelivery radiation therapy apparatuses, a treatment couch holding apatient may move through a rotating gantry to which an imaging deviceand radiation treatment source are mounted during treatment. In oneembodiment, the IGRT delivery system is a robotic arm based IGRTdelivery system, such as shown in FIG. 12.

In some implementations, one or more of methods 200-650 may be performedprior to the treatment stage (e.g., during treatment planning) and themethods of one or more of FIGS. 7-9C may be performed for the treatmentstage after one or more of methods 200-650 have been performed.Alternatively, the methods of one or more of FIGS. 7-9C may be performedwithout first performing method 200, for example.

The alignment phase of a treatment stage involves aligning a patient foran IGRT delivery system. The patient may be placed onto a treatmentcouch, and one or more images may be taken of the patient by an imagingdevice at various angles. If a motion model is to be used for trackingof a target, the alignment phase may also include generating the motionmodel based on tracking images taken from the various angles and/orupdating a previously generated motion model based on the trackingimages. For example, a first series of images of the patient may betaken at a first angle to capture different phases of a cyclic motionfrom the first angle and a second series of images may be taken at asecond angle to capture the different phases of the cyclic motion fromthe second angle. These images may then be used to generate or update amotion model.

FIG. 7 illustrates a method 700 of selecting a set of angles for use bya rotational imaging device during an alignment phase or a treatmentphase of a treatment stage, in accordance with one embodiment of thepresent invention.

At block 705 of method 700 processing logic determines a set of anglesthat have a tracking quality metric value that satisfies trackingquality metric criteria. In one embodiment, one or more of methods200-650 are performed at block 705 to determine the set of angles.Alternatively, one or more of methods 200-650 may have previously beenperformed during treatment planning. In such an instance, determiningthe set of angles may include receiving a list that includes the set ofangles and/or reviewing the list. Additionally, or alternatively, theset of angles may be based at least in part on angles that weresuccessfully used in previous treatment stages for the patient. Forexample, processing logic may build a histogram of angles that have beensuccessfully used during various treatment stages for the patient. Thisinformation may be used to adjust the tracking quality metric values forthe angles in the set of angles. For example, processing logic mayincrease the tracking quality metric values for angles that weresuccessfully used in previous treatment stages in some embodiments.Processing logic may favor angles that have been successfully used overother angles from the set of angles in other ways as well.

At block 710, processing logic begins an alignment phase or a treatmentphase for a treatment stage (also referred to as a treatment fraction)of a target. Method 700 may be performed during the alignment phase andagain during the treatment phase in some embodiments.

At block 715, processing logic selects a first angle from the set ofangles for a first rotation of a rotatable gantry to which an imagingdevice is mounted. In one embodiment, the first angle is an angle fromthe set of angles having a highest tracking quality metric value. In oneembodiment, the first angle is an angle from the set of angles that iswithin a first angle range (e.g., 30-60 degrees) and that has thehighest tracking quality metric value in that angle range. At block 720,the imaging device mounted to the rotatable gantry generates a firsttracking image of the target from the first angle during a firstrotation of the gantry.

At block 725, processing logic selects a second angle from the set ofangles for the first rotation of the rotatable gantry to which theimaging device is mounted. In one embodiment, the second angle isseparated from the first angle by at least 15 degrees (e.g., the firstangle may be 15 degrees and the second angle may be 30 degrees or more).In a further embodiment, the second angle is separated from the firstangle by at least 30 degrees. In a further embodiment, the second angleis separated from the first angle by about 70-110 degrees (e.g., byabout 90 degrees in one embodiment). In one embodiment, the second anglehas a second highest tracking quality metric value from the set ofangles. In one embodiment, the second angle has a highest trackingquality metric value from a subset of the set of angles that areseparated from the first angle by at least an angle separation threshold(e.g., by at least 15 degrees, at least 30 degrees, at least 60 degrees,etc.).

At block 730, the imaging device mounted to the rotatable gantrygenerates a second tracking image of the target from the second angleduring the first rotation of the gantry.

At block 735, processing logic may perform target tracking based on thefirst tracking image and the second tracking image. In some instances,performing target tracking may include generating or updating a motionmodel for the target using the tracking images. Additional angles mayalso be selected and used to generate additional tracking images of thetarget during the first rotation of the rotatable gantry. Additionally,or alternatively, the first and second angles and/or different anglesmay be used during future rotations of the rotatable gantry to generateadditional tracking images. These additional tracking images may be usedto continue to track the target. For example, two angles (e.g.,optionally separated by about 90 degrees) may be selected and used everyrotation of the rotatable gantry. In another example, three angles(e.g., optionally separated by 60 degrees) may be selected and usedevery rotation of the rotatable gantry. In another example, more thanthree angles may be selected and used every rotation of the rotatablegantry.

In one embodiment, to perform target tracking processing logic applies atarget tracking algorithm that performs image registration between atracking image taken at an imaging angle and a DRR generated from acorresponding angle of a CT scan or other treatment planning image ofthe patient. Based on the image registration the target trackingalgorithm may determine a location of the target as well as a shape ofthe target.

In one embodiment, processing logic applies the target trackingalgorithm to determine the position of the target in a first referenceframe represented by a tracking image, e.g., a live x-ray acquired fromthe imaging device in a treatment room reference frame, relative to asecond reference frame represented by a template of patches selectedfrom a second image, e.g., a DRR, where the location and shape of thetarget are known or defined in the second reference frame. The templatepatches are selected based on their perceived ability to distinguishcharacteristics of the target in the first image from nearby structures.The target's location in the first image is found by computingsimilarity values between each of several hypothetical, or candidate,locations for the target and the template patches. A maximum value ofthe similarity values indicates the location of the target in the firstimage.

A similarity value for a candidate location may be based on acombination of similarity values for each template patch at thecandidate location. “Similarity values” or “similarity measures” arenumbers that reflect the degree to which two images are similar to eachother. For example, a cross-correlation or combination of severalcross-correlations between two images can be used to compute asimilarity value. This combination of similarity values may be weightedaccording to the relative importance of the informational content aboutthe target among the template patches. In one embodiment, the similarityvalue for a candidate location, or template-level similarity value, is aweighted sum of the similarity values of the template patches, orpatch-level similarity values, at the candidate location. For example,the weighting applied can be a standard deviation of the pixel values inthe patches. As such, patches with a higher standard deviation are givengreater weight when computing the template-level similarity value. Othernumerical combinations and/or weightings of patch-level similarityvalues may be used to compute template-level similarity values.

One embodiment for locating a target proceeds by first assemblingpatch-level similarity maps over a tracking region for each patch. Eachpatch-level similarity map contains a similarity value for the patch ateach of the candidate locations considered in the image. The patch-levelsimilarity maps are then combined, according to their spatialrelationship in the template, to produce a global similarity map. Thecombination of the patch-level similarity maps may be a weighted sum ofthe similarity values in each patch-level similarity map.

In an alternative embodiment for locating a target, a template-levelsimilarity value for a candidate location is determined beforeproceeding to the next candidate location. Thus, in this alternativemethod, patch-level similarity maps are not used. Instead, the candidatelocations in the global similarity map are populated with template-levelsimilarity values as the template is moved from one candidate locationto another.

Similarity values as described above may be computed using, but notlimited to, a cross-correlation, entropy, mutual information, gradientcorrelation, pattern intensity, gradient difference, or image intensitygradients methods. The computed values may be normalized so that theresulting similarity value is a number ranging between 0 and 1 or −1 and1.

FIG. 8 illustrates a method 800 of using a set of angles by a rotationalimaging device during a treatment phase of a treatment stage for patienttreatment, in accordance with one embodiment of the present invention.At block 805, processing logic determines a set of angles that arecandidates for use during the treatment stage generate tracking images.The set of angles may be determined based on a received list thatincludes the set of angles (e.g., an ordered list ranking angles fromhighest tracking quality metric value to lowest tracking quality metricvalue). Alternatively, or additionally, the set of angles may bedetermined by performing the operations of one or more of methods200-650.

At block 810, processing logic selects a first angle from the set ofangles for a first rotation of the gantry. The first angle may be anangle having a highest tracking quality metric value, or may be an anglewithin a rotational range (e.g., from angles 0-90 degrees) having ahighest tracking quality metric value. At block 815, processing logiccauses an imaging device (e.g., an x-ray source and detector pair) togenerate a tracking image of the target from the selected angle duringthe first rotation of the gantry. At block 820, processing logicattempts to perform target tracking based at least in part on thetracking image. In one embodiment, one or more previous images mayalready have been taken during the treatment phase and/or during analignment phase of the treatment stage. In such an instance, the targettracking may be performed based on the first image and the one or moreprevious images.

At block 825, processing logic determines whether tracking wassuccessful using the first tracking image. Tracking may be successful ifthe target was successfully identified in the first image. If thetracking was successful, the method continues to block 830.

At block 830, processing logic determines whether the treatment stage iscomplete. If the treatment stage is complete, the method ends. If thetreatment stage is not complete, the method returns to block 810 andanother angle is selected and then used to generate another trackingimage. The other tracking image may be generated during the firstrotation of the rotational gantry or during a subsequent rotation of therotatable gantry.

If at block 825 it is determined that tracking was not successful, thenthe method proceeds to block 835. Tracking may be unsuccessful if atarget tracking algorithm is unable to identify the target in thetracking image. At block 835, processing logic determines a time windowwithin which a next tracking image should be taken. Target tracking maybe performed to update a motion model of the target. If the targettracking is unsuccessful for a most recent tracking image, oldertracking images may continue to be relied upon for the motion model.However, over time the accuracy of the motion model may degrade withoutupdated tracking images. The size of the time window may be dependent onhow quickly an accuracy of the motion model degrades over time. Forexample, if a target is susceptible to quasi-static motion, then themotion model's accuracy may degrade quickly. If the target issusceptible to cyclic motion, then the motion model's accuracy maydegrade more slowly.

A quasi-static motion model (also referred to as an episodic motionmodel) models the target as having a static position at its last viewedposition. Each time the target is imaged, the target location is updatedto a newly observed location, and is then assumed to stay at thatlocation until a new location is detected. A typical imaging time windowfor a quasi-static motion model may be between about 1-30 seconds,depending on dose delivery rate and other clinical factors such asexpected amplitude of episodic motion, expected probability of episodicmotion, and distance of treatment target from highly sensitive or atrisk organs.

A cyclic motion model (also referred to as a periodic motion model)models the target as continuously moving through a cyclic motion such ascaused by breathing or a beating heart. For a cyclic motion model thetime window may be on the order of about 5-120 seconds. The time windowmay vary depending on patient characteristics. For example, the timewindow may be about 15-60 seconds for some patients, and may be about90-120 seconds for other patients. In an example, if a patient breathesvery regularly, is relaxed, and does not move spontaneously or cough,the motion model may use very minimal adjustments through the entiretreatment and may therefore use a window of about 90-120 seconds.

At block 840, processing logic determines whether another angle from theset of angles will be available within the time window. The rotationalgantry may rotate at a predetermined speed. For example, the rotatablegantry may rotate at a speed of 2 rotations per minute, 5 rotations perminute, 10 rotations per minute, 15 rotations per minute, 20 rotationsper minute, or faster. The number of available angles from the set ofangles may vary on the speed of rotation and the size of the timewindow.

Assume for an example that the time window is 2 seconds, that theimaging device is at angle 5 degrees when the tracking fails for a mostcurrent tracking image, that the rotational gantry rotates at a speed of5 rotations per minute, and that a next angle in the set of angles is at38 degrees. At a rotational speed of 5 rotations per minute, therotational gantry is rotating 30 degrees every second. Accordingly, thenext angle in the set of angles at 38 degrees will be available withinthe time window. If, on the other hand, the next angle was at 80degrees, the next angle would be outside of the time window. If an angleis available in the time window, the method continues to block 845 andat block 845 treatment is continued and the method returns to block 810to select another angle. Otherwise the method proceeds to block 850.

In many instances multiple angles will be available within the timewindow. For example, at a rotational speed of 10 rotations per minutethe imaging devices sweeps all 360 degrees every 6 seconds. Accordingly,at that rotational speed a time window of 6 seconds would cause allangles from the set of angles to be available. If multiple angles areavailable, processing logic selects one of the available angles that areavailable within the time window at block 810. In one embodiment, anangle with a highest tracking quality metric value is selected. In oneembodiment, a next in time angle is selected. In one embodiment, a nextangle is selected based on a combination of when each of the angles willbe available and the tracking quality metrics of those angles. In oneembodiment, an angle of a last tracking image in which the target wassuccessfully tracked is also taken into consideration. For example, itmay be preferable to select an angle that is 90 degrees apart from theangle of the last successful tracking image. In some embodimentsmultiple previous tracking images are taken into consideration forselection of a next imaging angle. For example, if there are imagescovering most patient breathing phases, the system may select a nextimage that will be taken at an angle that will correspond to a breathingphase for which a recent image has not been generated.

At block 850 treatment is interrupted. This may include stoppingrotation of the rotatable gantry and/or stopping delivery of a radiationtreatment beam. During the interruption one or more additional trackingimages of the target may be generated until the target is successfullylocated. Additionally, a motion model of the target may be updated. Thetreatment may then continue and the method may resume from block 810.

FIGS. 9A-9C illustrate various methods of selecting a set of angles fromwhich tracking images may be taken. These methods may be used alone orin combination at block 805 of method 800 and/or at block 705 of method700.

FIG. 9A illustrates a first method 900 of selecting a set of angles foruse by a rotational imaging device during a treatment stage, inaccordance with one embodiment of the present invention. At block 905 ofmethod 900, processing logic receives an initial list of candidateangles that have tracking quality metric values that satisfied one ormore tracking quality metric criteria during treatment planning. Atblock 910, processing logic causes an imaging device to generatepretreatment images of a target from one or more angles of the initiallist of candidate angles. These pretreatment images may be generatedduring an alignment phase of a treatment stage. At block 915, processinglogic determines a subset of the initial list of candidate angles forthe treatment stage based on the pretreatment images. For example,processing logic may sample 10%-50% of the candidate angles and updatetracking quality metric values for those sampled angles. Processinglogic may additionally interpolate changes to the non-sampled anglesbased on the changes to the sampled angles.

FIG. 9B illustrates a second method 920 of selecting a set of angles foruse by a rotational imaging device during a treatment stage, inaccordance with one embodiment of the present invention. At block 925 ofmethod 900, processing logic performs a CT scan or MRI scan of a targetin a patient. At block 930, processing logic receives a delineation ofthe target in the CT scan or MRI scan.

At block 935, processing logic generates x-ray images of the target froma plurality of angles. At block 940, processing logic determines, foreach angle of the plurality of angles, the tracking quality metric valuefor tracking the target based on an analysis of an x-ray image generatedat that angle. The tracking quality metric value may be determined usingany of the aforementioned techniques, except that an x-ray is usedrather than a standard DRR to generate the tracking quality metricvalue. At block 945, processing logic then selects a subset of theplurality of angles that have a tracking quality metric value thatsatisfies the one or more tracking quality metric criteria.

FIG. 9C illustrates a third method 950 of selecting a set of angles foruse by a rotational imaging device during a treatment stage, inaccordance with one embodiment of the present invention. At block 955 ofmethod 950 processing logic receives an initial list of candidate anglesthat have a tracking quality metric value that satisfied one or moretracking quality metric criteria during treatment planning. At block960, processing logic generates a plurality of x-ray images of thetarget from two or more candidate angles. At block 965, processing logicgenerates a motion model of the target from the plurality of images. Themotion model may also be based at least in part on a general statisticalmotion model that describes how a particular type of target is likely tomove over time in some embodiments. At block 970, processing logicdetermines, for each angle in the list of candidate angles, a motionsensitivity at that angle. Processing logic then determines a subset ofthe initial list of candidate angles for the treatment stage that aremost sensitive to motion of the target as represented in the motionmodel.

FIGS. 10A-10C illustrate an IGRT delivery system 1000 that is capable ofcarrying out the functionalities described above with respect to theIGRT delivery system 104 of FIG. 1 according to one or more embodiments.The IGRT delivery system 1000 may be the IGRT delivery system referredto in methods 200-950 in various embodiments. FIG. 10A illustrates anaxial cut-away view of the gantry based image-guided radiation treatment(IGRT) system 100 according to one embodiment. FIG. 10B illustrates aside cut-away view of the gantry based IGRT delivery system 1000,according to one embodiment. FIG. 10C illustrates a perspective view ofthe rotatable gantry structure of the IGRT delivery system 1000,according to one embodiment.

IGRT delivery system 1000 includes a gantry frame 1002 within which isdisposed a rotatable gantry structure 1004 configured to rotate around arotation axis 1014 that passes through an isocenter 1016. In theillustrated example, the rotatable gantry structure 1004 rotates in theclockwise direction 1099. However, the rotatable gantry structure mayalso rotate in a counter-clockwise direction. Associated with the IGRTdelivery system 1000 is an imaginary plane, termed herein a transverseisocentric plane 1017, that is orthogonal to the rotation axis 1014 andpasses through the isocenter 1016. The gantry frame 1002, the isocenter1016, the rotation axis 1014, and the transverse isocentric plane 1017may be fixed and motionless relative to a treatment vault (not shown) inwhich the IGRT delivery system 1000 is installed. As used herein, anisocenter or machine isocenter is a physical point in a treatment room(treatment vault). A treatment center is a point within the targetvolume defined by a physician during treatment planning, normally basedwithin the pretreatment treatment planning image reference frame (e.g.,pretreatment CT image reference frame). For isocentric treatment thetreatment center may be aligned with the machine isocenter during theset up procedure described above.

The rotatable gantry structure 1004 includes one or more beam members1006 that each extend between first ring member 1008 and second ringmember 1009 disposed approximately on opposite sides of the transverseisocentric plane 1017. Note that the two ends of a beam member may ormay not be 180 degrees apart. By offsetting the source and detector, afield of view may be increased. The first ring member 1008 correspondsgenerally to a first end of the rotatable gantry structure 1004 (towardthe left side of FIG. 10B), while the second ring member 1009corresponds generally to a second, opposite end of the rotatable gantrystructure 1004 (toward the right side of FIG. 10B). The first and secondring members 1008 and 1009 are supported at their respective ends of therotatable gantry structure 1004 by corresponding ends of the gantryframe 1002 in a manner that allows and facilitates rotation of therotatable gantry structure 1004 around the rotation axis 1014 whilekeeping the rotation axis 1014 highly stable and stationary. The skilledartisan will appreciate that any of a variety of different mechanicalsupport schemes that allow such rotation can be used (e.g.,anti-friction sleeves, slip bearings, roller bearings, etc.). Theskilled artisan will appreciate that the gantry frame 1002 can be madesubstantially thicker or otherwise reinforced at its respective endsthan is indicated schematically in FIG. 10B, in accordance with theparticular materials being used and other design considerations, forensuring such mechanical stability.

The rotatable gantry structure 1004 may contain two beam members 1006separated by approximately 180 degrees around the rotation axis 1014,which is useful (for example and without limitation) for facilitatingrotational balancing (e.g. by applying appropriate balancing weights tothe opposing beam members 1006). The skilled artisan will appreciatethat the term beam member as used herein can encompass a wide variety ofdifferent types of structural members (e.g., solid rods, hollow rods,assemblies of parallel or concentric rods, truss-type structures, etc.)that can structurally extend from one place to another and along whichone or more physical items (e.g., LINACs, LINAC assemblies, imagingsources, imaging detectors, and so forth) can be fixably or movablymounted or positioned.

Movably mounted on one of the beam members 1006 is a radiation treatmentsource 1010 (also referred to as a therapeutic radiation head), such asand without limitation a linear accelerator (LINAC) or a compact protonsource, which includes thereon an end collimator 1012, such as amulti-leaf collimator (MLC), and which provides a therapeutic radiationbeam 1003. The radiation treatment source 1010 is mounted to the beammember 1006 by a coupling device 1007 that is configured and adapted toachieve the translational and rotational functionalities describedfurther hereinbelow.

In one embodiment, the radiation treatment source 1010 comprises acompact lightweight LINAC, such as an X-band or C-band LINAC in acompact configuration without a bending magnet. This allows a compactsystem design in which all moving components are behind a fixed surfacecovering (e.g., a bore shield), thus eliminating the risk of collisionwith the patient and enabling higher rotation speeds. In otheralternative embodiment, the compact accelerator can include a bendingmagnet.

The radiation treatment source 1010 could be a LINAC configured withdifferent secondary collimation systems 1012, including fixed cones, avariable aperture collimator such as the Iris Variable ApertureCollimator (Accuray Incorporated, Sunnyvale, CA), a binary collimator,or an MLC.

The rotatable gantry structure 1004 and radiation treatment source 1010are dimensioned so as to allow a central bore 1018 to exist. The centralbore 1018 may be an opening sufficient to allow a patient P to bepositioned therethrough without the possibility of being incidentallycontacted by the radiation treatment source 1010 or other mechanicalcomponents as the gantry rotates radiation head 1010 about patient P. Atreatment couch 1022 is provided for supporting the patient P. Thetreatment couch 1022 may be coupled to an automated patient positioningsystem (not shown) for moving the patient P into a therapy position andmanipulating the patient with three or more degrees of freedom (e.g.,three orthogonal translations, one parallel to the rotation axis 1014,two orthogonal to rotation axis 1014, plus optionally one or morerotations). The skilled artisan will appreciate that many couches can beused in accordance with embodiments of the present invention.

According to one embodiment, a cylindrically shaped bore shield 1020 isprovided to line the boundary of the central bore 1018. In addition topreventing unexpected movement of the patient's hands or other body partinto collision with moving parts, the bore shield 1020 can reduce thesense of intimidation that the patient might feel in view of the largemoving parts in the device. The bore shield 1020 provides the ability tomaximize the rotation speed of the gantry, while still meeting allregulatory safety requirements. The bore shield 1020 should be formed ofa material that is substantially transparent to the therapeutic andimaging radiation, and optionally can be visibly opaque as well.

According to one embodiment, the radiation treatment source 1010 ismounted to the beam member 1006 in a manner that allows and facilitates(i) translation of the radiation treatment source 1010 along the beammember 1006 (i.e., in an end-to-end manner between first ring member1008 and second ring member 1009), (ii) pivoting of the radiationtreatment source 1010 around a first pivot axis M1, termed herein aprimary pivot axis, and (iii) pivoting of the radiation treatment source1010 around a second axis M2, termed herein a secondary pivot axis,located at a right angle to M1. Preferably, the axes M1 and M2 each passthrough the center of mass (CoM) of the radiation treatment source 1010,and the center of mass lies along the axis of the therapeutic radiationbeam 1003. Collectively, the primary pivoting around axis M1 and thesecondary pivoting around axis M2 can be considered as a gimbal orgimballing motion of the radiation treatment source 1010.

The skilled artisan will appreciate that the IGRT delivery system 1000further includes a plurality of actuators of various types (not shown)for achieving the mechanical functionalities described hereinabove andhereinbelow in the instant disclosure. Thus, for example, the IGRTdelivery system 1000 includes respective actuation devices (not shown)to achieve the rotation of the rotatable gantry structure 1004 aroundthe rotation axis 1014, the axial translation of the radiation treatmentsource 1010 along the beam member 1006, the M1 pivoting of the radiationtreatment source 1010, and the M2 pivoting of the radiation treatmentsource 1010. The IGRT delivery system 1000 further includes one or moreprocessing devices and/or control units, such as may be implemented onone or more programmable computers, for controlling the variousactuators and sending signals to and from the various recited radiationsources and detectors as necessary to achieve the functionalitiesdescribed hereinabove and hereinbelow in the instant disclosure.

Advantageously, by virtue of the possibilities provided by thecombination of axial translation of the radiation treatment source 1010,M1 pivoting, and M2 pivoting, a rich variety of radiation treatmentdelivery plans are facilitated by the IGRT delivery system 1000, asdiscussed herein. At the same time, by virtue of a ring-style mechanicalnature of the rotatable gantry structure 1004 (which could be moreparticularly referenced as a “barrel-style” mechanical nature), agreater degree of mechanical stability may be provided in comparison toapproaches in which radiation treatment source support is of acantilever-like nature. Generally speaking, in addition to positivelyaffecting the range of achievable tilt angles (i.e., the angle betweenthe therapeutic radiation beam 1003 and the transverse isocentric plane1017 when the therapeutic radiation beam is isocentric), increasedend-to-end distance between the ring members will have an impact on themechanical stability of the device.

The phrases “rotating the gantry” or “gantry rotation” refer to rotationof the rotatable gantry structure 1004. Advantageously, there are manypossible modes of operation for the IGRT delivery system 1000. Therotatable gantry structure 1004 (and thus the radiation treatment source1010 mounted thereto) may continuously rotate during treatment.Alternatively, the rotatable gantry structure 1004 may be rotated to andparked at a particular angle. The radiation treatment source 1010 canrotate about the patient without tilting off axis. In this case it couldtreat at a discrete set of fixed gantry rotation angles (coplanar beams)with or without irregular field shaping and with or without modulation,thus enabling coplanar static beams, CRT, and IMRT. For each fixedgantry rotation angle, the radiation treatment source 1010 can be tiltedoff axis at a tilt angle, thus enabling non-coplanar CRT and IMRT.Alternatively, the radiation treatment source 1010 could be configuredwith a binary collimator or an MLC and deliver radiation whilecontinuously rotating without tilting off axis. By combining theradiation treatment source 1010 rotation with patient movement throughthe central bore 1018, which can be accomplished for example by lineartranslation of the treatment couch 1022, sequential or helicalTomoTherapy is enabled. Alternatively, the radiation treatment source1010 could be configured with a MLC and deliver radiation while rotatingthe gantry without tilting off axis.

The gantry rotation speed, dose rate, MLC shapes, and collimator anglecould be varied during gantry rotation, thus also enabling conventionalcoplanar rotational arc therapy. By also tilting the radiation treatmentsource 1010 off axis as the gantry angle is varied, it is possible todeliver rotational arc therapy with multiple non-coplanar rotations inorder to maximize the number of beam positions, the solid angle coveredby these positions, and the degree of intensity or fluence modulation inorder to achieve the highest possible treatment plan quality. In oneapproach, the tilt angle is held constant while the gantry angle isvaried. In another approach, the tilt angle is varied while the gantryangle is also varied (referred to as conical non-coplanar rotational arctherapy and cono-helical rotational arc therapy). This approach could becombined with movement of the treatment couch 1022 during gantryrotation to provide what is termed herein conical non-coplanartomotherapy or cono-helical non-coplanar tomotherapy. Because of theability to achieve many orientations using gantry rotation (between 0and 360 degrees) and moving the source out of plane by varying the tiltangle (within the maximum limits of the system, which could for examplebe −30 to +30 degrees, or −45 to +45 degrees), breast treatments withparallel opposed fields could be easily and quickly performed by settingthe appropriate gantry rotation and tilt angles.

The rotatable gantry structure 1004 is further provided with additionalbeam members 1060 extending between ring members 1008 and 1009. Theadditional beam members 1060 are each provided with one (or more)imaging devices (e.g., kV source(s)) 1052 and/or one (or more) imagingdetectors (e.g., kV detectors) 1054, and are configured such that eachimaging device 1052 is paired with an associated imaging detector 1054approximately opposite the isocenter. Each imaging device 1052 iscoupled to its respective beam member 1006 by a respective couplingdevice 1056, and each imaging detector 1054 is coupled to its respectivebeam member 1006 by a respective coupling device 1058, the couplingdevices 1056 and 1058 being configured and adapted to achieve thefunctionalities (e.g., fixed, translational, and/or rotational)described further herein. The beam members 1060 are disposed at suitableangles relative to each other and to the radiation treatment source 1010to achieve the desired imaging functionality, which can include standardx-ray imaging or stereoscopic imaging when combined with rotation of theimaging device about the patient. In one embodiment, the imaging devices1052 are mounted orthogonally to the radiation head 1010 on the gantry.This enables the imaging devices 1052 to generate images of a targetwhile the radiation treatment source 1010 delivers a radiation beam tothe target.

With one or more imaging device 1052, the system can acquire X-rayimages during gantry rotation. Because of the ability to achieve anyorientation defined by a gantry rotation angle and a tilt angle, allrotation offsets can be handled by adjusting the rotation and tiltangles appropriately. Two imaging devices 1052 are shown. However, itshould be understood that the IGRT delivery system 1000 may insteadinclude a single imaging device 1052. With two or more kV imagingsystems, the system can acquire stereo x-ray images simultaneously atany gantry rotation angle. With one kV imaging system, the system canacquire stereo x-ray images non-simultaneously at different gantryrotation angles (separated for example by 90 degrees). X-ray images canbe used for patient set up for example by registration of the x-rayimages to digitally reconstructed radiographs (DRRs) generated from theplanning CT image. If two imaging devices 1052 are used, the imagingdevices 1052 may be mounted approximately perpendicular to each other.

The ability to generate intra-treatment images allows for intra-fraction(e.g., in-treatment) target motion tracking. Intra-fraction motiontracking and correction helps enable better treatment plans and theaccurate delivery of those treatment plans. A system for correlatingtarget motion with motion of an anatomical feature of the body (forexample and without limitation external chest wall or a moving boneystructure) can also be included in embodiments of the present invention.For example, a lung tumor will move periodically with respiration, andthe tumor location can be correlated with (for example and withoutlimitation) motion of the chest wall as the patient breaths.

FIG. 11 illustrates a perspective view of a gantry based IGRT deliverysystem 1100 and a schematic diagram of a computer system 1150 integraltherewith and/or coupled thereto, according to one embodiment. Computersystem 1050 uses one or more busses, networks, or other communicationssystems 1160, including wired and/or wireless communications systems.Computer system 1150 can operate in conjunction with the the IGRTdelivery system 1100 to implement the methods of one or more of theembodiments described herein. Methods of image guided radiationtreatment in accordance with one or more of the embodiments may beimplemented in machine readable code (i.e., software or computer programproduct) and performed on computer systems such as, but not limited to,the computer system 1150. Computer system 1150 may include a processingdevice (e.g., a general purpose or special purpose processing device)1152, random access memory 1153, and nonvolatile memory 1154 (e.g.,electromechanical hard drive, solid state drive, etc.). Computer system1150 may also include various input/output devices, such as a displaymonitor 1155, a mouse 1161, a keyboard 1163, and other I/O devices 1156capable of reading and writing data and instructions from non-transitorycomputer readable storage media 1158 such as tape, compact disk (CD),digital versatile disk (DVD), blu-ray disk (BD), memory, a hard diskdrive, and so forth.

In addition, there may be connections via the one or more busses,networks, or other communications systems 1160 to other computers anddevices, such as may exist on a network of such devices, e.g., theInternet 1159. Software to control the image guided radiation treatmentoperations described herein may be implemented as a program product andstored on a tangible storage device such as the non-transitory computerreadable storage medium 1158, an external nonvolatile memory device1162, or other tangible storage medium.

The gantry based IGRTs 1000, 1100 may be used for coplanar rotationalarc therapy as well as for non-coplanar rotational arc therapy (e.g.,helical delivery). In one embodiment, conical non-coplanar rotationalarc therapy may be performed by the gantry based IGRTs 1000, 1100. Forconical non-coplanar rotational arch therapy, the radiation treatmentsource 1010 is axially translated along the beam member 1006 in discretesteps, with a gantry rotation occurring at each step. There can bediscrete firings of the therapeutic radiation beam at respectivediscrete gantry angles, or there can be continuous firings of thetherapeutic radiation beam as the gantry angle is continuously changed,each of which are within the scope of the present teachings. In oneembodiment, cono-helical non-coplanar rotational arc therapy and othertypes of helical rotational arc therapy may be performed by the gantrybased IGRTs 1000, 1100. For cono-helical non-coplanar rotational arctherapy, the radiation treatment source 1010 is translated along thebeam member 1006 as the gantry is rotated. There can be discrete firingsof the therapeutic radiation beam at respective discrete gantry angles(and correspondingly discrete translational advances of the radiationtreatment source 1010), or there can be continuous firings of thetherapeutic radiation beam as the gantry angle is continuously changed(and correspondingly continuous translational advances of the radiationtreatment source 1010), each of which are within the scope of thepresent teachings. Cono-helical non-coplanar rotational arc therapy mayspan the same conical three-dimensional volume as conical non-coplanarrotational arc therapy, but does so in a continuous or helical manner.

A rich variety of radiation therapy profiles and strategies can beaccommodated using the gantry based IGRT delivery systems 1000, 1100.Such possibilities include, but are not limited to: single or parallelopposed static beams with rectangular field shaping and 1D (wedge orvirtual wedge using MLC) intensity modulation; static beams withrectangular field shaping and 1D modulation; coplanar rotationaltreatments (“arc therapy”) with rectangular field shaping and 1Dmodulation; coplanar or non-coplanar beams with irregular field shapingand 1D modulation (“conformal radiation therapy” or CRT); coplanar ornon-coplanar beams with irregular field shaping and 2D modulation(“intensity modulated radiation therapy” or IMRT); and tomotherapy(helical or sequential) with coplanar rotation using a narrow beam incombination with couch movement and 2D modulation. Such possibilitiesfurther include rotational arc therapy, also called intensity modulatedarc therapy (IMAT), including one or more coplanar rotations, irregularfield shaping, and 2D modulation, with gantry rotation speed, dose rate,MLC positions, and in some cases collimator angles being varied duringrotation, and including multiple rotations that increase the achievabledegree of intensity modulation in view of practical constraints on MLCmotion during treatment.

One of the benefits of the gantry based IGRT delivery systems 1000, 1100is achieving rotational arc therapy with multiple non-coplanar rotationsin order to maximize the number of beam positions, the solid anglecovered by these positions, and the degree of intensity or fluencemodulation of the therapeutic radiation beam in order to achieve thehighest possible treatment plan quality. The gantry based IGRT deliverysystems 1000, 1100 may rotate the gantry at a rate of anywhere from 1-50rotations per minute or faster in embodiments. In some embodiments thegantry may alternatively be rotated slower than 1 rotation per minute.In one embodiment, the gantry is rotated at a rate of 1-10 rotations perminute. In one embodiment, the gantry is rotated at a rate of about 3-6rotations per minute. The faster the rotation of the gantry, the greaterthe freedom to select imaging angles. For example, if the gantry doublesits rotational speed, then the number of good imaging angles needed toprovide the same temporal sampling is halved. In one embodiment, thegantry is rotated at a rate of about 5 rotations per minute. Another ofthe benefits of the gantry based IGRT delivery systems 1000, 1100 isaccurate delivery of treatment plans using image guidance for patientset up and intra-fraction motion tracking and correction. Another of thebenefits of the gantry based IGRT delivery systems 1000, 1100 isincreased rigidity, which enables higher rotation speeds, higherdelivery accuracy (less error in radiation beam position andorientation), and higher 3D reconstructed image quality (less error inimaging system geometry during rotation).

FIG. 12 illustrates a perspective view of a robotic arm based IGRTdelivery system 1200, according to a one embodiment. In the illustratedembodiment, the IGRT delivery system 1200 includes a linear accelerator(LINAC) 1201 that acts as a radiation treatment source. The LINAC 1201is mounted on the end of a robotic arm 1202 having multiple (e.g., 5 ormore) degrees of freedom in order to position the LINAC 1201 toirradiate a pathological anatomy (e.g., target 1220) with beamsdelivered from many angles, in many planes, in an operating volumearound a patient. Treatment may involve beam paths with a singleisocenter, multiple isocenters, or with a non-isocentric approach.

The LINAC 1201 may be positioned at multiple different nodes (predefinedpositions at which the robot stops and radiation may be delivered)during treatment by moving the robotic arm 1202. At the nodes, the LINAC1201 can deliver one or more radiation treatment beams to target 1220.The nodes may be arranged in an approximately spherical distributionabout a patient. The particular number of nodes and the number oftreatment beams applied at each node may vary as a function of thelocation and type of pathological anatomy to be treated. For example,the number of nodes may vary from 50 to 300, or more preferably 15 to100 nodes and the number of beams may vary from 1100 to 3200, or morepreferably 50 to 300.

Robotic arm based IGRT delivery system 1200, in accordance with oneembodiment of the present invention, includes an imaging system 1210having a processing device 1230 connected with imaging sources 1203A and1203B (also referred to as imaging devices) and detectors 1204A and1204B. The imaging sources 1203A-1203B may be x-ray sources, and thedetectors 1204A-1204B may be x-ray detectors. The imaging sources1203A-1203B and/or imaging detectors 1204A-1204B may have fixedpositions at fixed predetermined angles. In one embodiment, the imagingsources 1203A-1203B are approximately orthogonal (e.g., have an angularseparation of about 90 degrees) to facilitate stereo-imaging.Alternatively, the imaging sources 1203A, 1203B and/or imaging detectors1204A, 1204B may be mobile, in which case they may be repositioned tomaintain alignment with the target 1220.

The two imaging sources 1203A and 1203B may be mounted in fixedpositions on the ceiling of an operating room and may be aligned toproject x-ray imaging beams from two different angular positions (e.g.,separated by 90 degrees) to intersect at a machine isocenter (referredto herein as a treatment center, which provides a reference point forpositioning the patient on a treatment couch 1206 during treatment) andto illuminate imaging planes of respective detectors 1204A and 1204Bafter passing through the patient 1225. Imaging system 1210, thus,provides stereoscopic imaging of the target 1220 and a surroundingvolume of interest (VOI).

In some embodiments, robotic arm based IGRT delivery system 1200includes a secondary imaging system 1239. The secondary imaging system1239 may include a rotatable gantry 1240 (e.g., a ring) attached to arobotic arm (not shown). The robotic arm may move the rotatable gantry1240 along one or more axes (e.g., along an axis that extends from ahead to a foot of the treatment couch 1206. An imaging source 1245 and adetector 1250 are mounted to the rotatable gantry 1240. The rotatablegantry 1240 may rotate 360 degrees about the axis that extends from thehead to the foot of the treatment couch. Accordingly, the imaging source1245 and detector 1250 may be positioned at numerous different angles.In one embodiment, the imaging source 1245 is an x-ray source and thedetector 1250 is an x-ray detector. In one embodiment, the secondaryimaging system 1239 includes two rings that are separately rotatable.The imaging source 1245 may be mounted to a first ring and the detector1250 may be mounted to a second ring. In one embodiment, the rotatablegantry 1240 rests at a foot of the treatment couch during radiationtreatment delivery to avoid collisions with the robotic arm 1202.

Detectors 1204A, 1204B, 1250 may be fabricated from a scintillatingmaterial that converts x-rays to visible light (e.g., amorphoussilicon), and an array of CMOS (complementary metal oxide silicon) orCCD (charge-coupled device) imaging cells that convert the light to adigital image that can be compared with a reference image during animage registration process that transforms a coordinate system of thedigital image to a coordinate system of the reference image, as is wellknown to the skilled artisan. The reference image may be, for example, adigitally reconstructed radiograph (DRR).

FIG. 13 illustrates example tracking quality metric values for 72 testedangles, according to a one embodiment. FIG. 13 additionally illustratesan example tracking quality metric threshold. Angles having trackingquality metric values that are above the tracking quality metricthreshold may be included in a set of candidate angles for use in imagetracking during a treatment stage, while angles below the trackingquality metric threshold may not be included. In this example, a goodcandidate pair of images (each having tracking quality metric values orconfidence values of over 0.8 or 80%) is 45 degrees and 135 degrees,which results in two consecutive orthogonal images.

FIG. 14 illustrates an example patient 1405 along with angles 1425 thatcan be used to successfully track a target 1410 in the patient andangles 1430 that cannot be used to successfully track the target 1410,according to a one embodiment. As shown, for angles 1430 the patient'sspine 1415 is blocking the target 1410. Accordingly, the target 1410 maynot be discernable in x-ray images taken at angles 1430. Angles 1430cover approximately 120 degrees of rotation that should be avoided inthis example. However, the spine 1415 does not overlap the target 1410at angles 1425, and so angles 1425 may be included in a set of candidateangles for use in tracking the target 1410 during a treatment stage.Angles 1425 amount to approximately 240 degrees of rotation that areavailable for imaging in this example.

It will be apparent from the foregoing description that aspects of thepresent invention may be embodied, at least in part, in software. Thatis, the techniques may be carried out in a computer system or other dataprocessing system in response to its processor, such as processingdevice 120 of system controller 114 and/or processing device 170 oftreatment planning system 118 of FIG. 1, for example, executingsequences of instructions contained in a memory. In various embodiments,hardware circuitry may be used in combination with software instructionsto implement embodiments of the present invention. Thus, the techniquesare not limited to any specific combination of hardware circuitry andsoftware. In addition, throughout this description, various functionsand operations may be described as being performed by or caused bysoftware code to simplify description. However, those skilled in the artwill recognize what is meant by such expressions is that the functionsresult from execution of the code by a processing device such asprocessing device 120 or 170.

A computer-readable medium can be used to store software and data whichwhen executed by a general purpose or special purpose processing devicecauses the processing device to perform various methods of the presentinvention. This executable software and data may be stored in variousplaces including, for example, system memory and storage or any otherdevice that is capable of storing software programs and/or data. Thus, acomputer-readable medium includes any non-transitory mechanism thatprovides (i.e., stores) information in a form accessible by a machine(e.g., a computer, network device, personal digital assistant,manufacturing tool, any device with a set of one or more processors,etc.). For example, a computer-readable medium includesrecordable/non-recordable media such as read only memory (ROM), randomaccess memory (RAM), magnetic disk storage media, optical storage media,flash memory devices, etc.

Unless stated otherwise as apparent from the foregoing discussion, itwill be appreciated that terms such as “determining,” “computing,”“generating,” “comparing,” “selecting,” “receiving,” or the like mayrefer to the actions and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (e.g., electronic) quantities within thecomputer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system's memoriesor registers or other such information storage or display devices.Embodiments of the methods described herein may be implemented usingcomputer software, firmware, hardware, or a combination thereof. Ifwritten in a programming language conforming to a recognized standard,sequences of instructions designed to implement the methods can becompiled for execution on a variety of hardware platforms and forinterface to a variety of operating systems. In addition, embodiments ofthe present invention are not described with reference to any particularprogramming language. It will be appreciated that a variety ofprogramming languages may be used to implement embodiments of thepresent invention.

It should be noted that the methods and apparatuses described herein arenot limited to use only with medical diagnostic imaging and treatment.In alternative embodiments, the methods and apparatus herein may be usedin applications outside of the medical technology field, such asindustrial imaging and non-destructive testing of materials. In suchapplications, for example, “treatment” may refer generally to theeffectuation of an operation controlled by the treatment planningsystem, such as the application of a beam (e.g., radiation, acoustic,etc.) and “target” may refer to a non-anatomical object or area.

In the foregoing specification, embodiments of the invention have beendescribed with reference to specific examples. It will, however, beevident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the invention asset forth in the appended claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A method comprising: determining, by a processingdevice, a plurality of angles from which tracking images can begenerated by an imaging device; generating, by the processing device, aplurality of projections of a treatment planning image, the treatmentplanning image comprising a delineated target, wherein each projectionof the plurality of projections has an angle that corresponds to one ofthe plurality of angles from which the tracking images can be taken;determining, for each angle of the plurality of angles, a value of atracking quality metric for tracking the target based on an analysis ofa projection generated at that angle; and selecting, by the processingdevice, a subset of the plurality of angles that have a tracking qualitymetric value that satisfies a tracking quality metric criterion, one ormore angles of the subset to be used to generate a tracking image of thetarget during a treatment stage.
 2. The method of claim 1, wherein thetreatment planning image comprises a magnetic resonance imaging (MRI)scan or a computer tomography (CT) scan of the patient, the methodfurther comprising: generating the MRI scan or the CT scan of thepatient; and receiving a delineation of the target in the MRI scan orthe CT scan.
 3. The method of claim 1, wherein generating the pluralityof projections comprises generating a plurality of digitallyreconstructed radiographs (DRRs).
 4. The method of claim 3, wherein thetreatment planning image is a three-dimensional ( 3D) treatment planningimage, and wherein generating a DRR for a particular angle of theplurality of angles comprises: generating a target region DRR for theparticular angle based on tracing rays at the particular angle through aregion of the 3D treatment planning image that includes the delineatedtarget, wherein additional regions of the 3D treatment planning imagethat do not include the delineated target are excluded from the tracedrays; and generating a standard DRR for the particular angle based ontracing rays at the particular angle through the 3D treatment planningimage.
 5. The method of claim 4, wherein determining the trackingquality metric for the particular angle comprises: searching for thetarget in the standard DRR using the target region DRR based on a targettracking algorithm that will be used to track the target from trackingimages during the treatment of the patient, wherein a failure toidentify the target in the standard DRR from the searching causes theparticular angle to have a particular tracking quality metric value thatfails to satisfy the tracking quality metric criterion.
 6. The method ofclaim 5, wherein searching for the target in the standard DRR using thetarget region DRR comprises performing pattern matching to find one ormore patterns in the standard DRR that match a pattern of the target inthe target region DRR, wherein identification of multiple patterns inthe standard DRR that match the pattern of the target in the targetregion DRR causes the particular angle to have a particular trackingquality metric value that fails to satisfy the tracking quality metriccriterion.
 7. The method of claim 5, wherein searching for the target inthe standard DRR using the target region DRR comprises performingpattern matching to find a first pattern in the standard DRR thatmatches a second pattern of the target in the target region DRR, whereinthe tracking quality metric value is proportional to a degree ofsimilarity between the first pattern and the second pattern.
 8. Themethod of claim 3, wherein determining the tracking quality metric valuefor a particular angle of the plurality of angles comprises: determininga contrast between the delineated target and a surrounding region of thedelineated target in a DRR of the plurality of DRRs that is generatedfor the particular angle; and determining the tracking quality metricvalue for the particular angle based at least in part on the contrast,wherein the tracking quality metric criterion comprises at least one ofa minimum acceptable contrast or a minimum acceptable contrast to noiseratio.
 9. The method of claim 3, wherein determining the trackingquality metric value for a particular angle of the plurality of anglescomprises: determining an edge of the delineated target in a DRR of theplurality of DRRs that is generated for the particular angle;determining an edge strength for the edge; and determining the trackingquality metric value for the particular angle based at least in part onthe edge strength, wherein the tracking quality metric criterioncomprises a minimum acceptable edge strength.
 10. The method of claim 3,wherein the treatment planning image further comprises an additionaldelineated structure, and wherein determining the tracking qualitymetric value for the particular angle comprises: determining whetherthere is an overlap between the delineated target and the additionaldelineated structure in a DRR of the plurality of DRRs that is generatedfor the particular angle, wherein an overlap between the delineatedtarget and the additional delineated structure causes the trackingquality metric value for the particular angle to fail to satisfy thetracking quality metric criterion.
 11. The method of claim 1, whereinthe subset comprises at least a first angle and a second angle that isat least separated by a minimum threshold from the first angle.
 12. Themethod of claim 1, wherein the treatment planning image comprises acomputer tomography (CT) scan, and wherein generating a projection ofthe plurality of projections for a particular angle of the plurality ofangles comprises: tracing a ray through the CT scan at the particularangle, wherein the ray passes through the delineated target;accumulating CT values for the ray as the ray traverses the CT scan; andgenerating an effective depth value based on an accumulation of the CTvalues, wherein the effective depth value represents a total accumulateddensity of material traversed by the ray, and wherein the trackingquality metric criterion comprises a maximum acceptable effective depthvalue.
 13. The method of claim 1, wherein the plurality of angles are ina 360 degree arc around the patient.
 14. The method of claim 1, whereinthe treatment planning image is a three-dimensional ( 3D) treatmentplanning image, and wherein the target comprises a plurality offiducials, and wherein generating a projection of the plurality ofprojections for a particular angle of the plurality of angles comprises:projecting positions of the plurality of fiducials from a 3D space ofthe 3D treatment planning image at the particular angle onto atwo-dimensional ( 2D) virtual detector plane.
 15. The method of claim14, wherein determining the tracking quality metric value for theparticular angle comprises: determining whether a first fiducial of theplurality of fiducials overlaps a second fiducial of the plurality offiducials in the virtual 2D detector plane, wherein an overlap betweenthe first fiducial and the second fiducial causes the tracking qualitymetric value for the particular angle to fail to satisfy the trackingquality metric criterion.
 16. The method of claim 14, whereindetermining the tracking quality metric value for the particular anglecomprises determining an amount of separation between the plurality offiducials in the virtual 2D detector plane, and wherein the angles inthe subset are angles having a maximum amount of separation between theplurality of fiducials.
 17. The method of claim 1, further comprising:ordering the plurality of angles based on the tracking quality metric,wherein the subset of the plurality of angles comprises angles havinghighest tracking quality metric values.
 18. The method of claim 1,wherein the imaging device is an x-ray device of a helical deliveryradiation therapy apparatus that comprises a treatment couch that willmove during patient treatment by the helical delivery radiation therapyapparatus, the method further comprising: generating a first pluralityof projections of the treatment planning image for a first couchposition of the treatment couch, wherein each projection of the firstplurality of projections is generated for one of the plurality ofangles; determining, for each angle of the plurality of angles, thetracking quality metric value at the first couch position based on theanalysis of the projection generated at that angle and the first couchposition; generating a second plurality of projections of the treatmentplanning image for a second couch position of the treatment couch,wherein each projection of the second plurality of projections isgenerated for one of the plurality of angles; determining, for eachangle of the plurality of angles, the tracking quality metric value atthe second couch position based on the analysis of the projectiongenerated at that angle and the second couch position; and selecting thesubset of the plurality of angles from which to generate the trackingimage of the target during the treatment stage based on the trackingquality metric values at the first couch position and the trackingquality metric values at the second couch position.
 19. The method ofclaim 1, wherein the treatment planning image comprises afour-dimensional (4D) treatment planning image, the method furthercomprising: generating a first plurality of projections of the 4Dtreatment planning image for a first time of the 4D treatment planningimage, wherein each projection of the first plurality of projections isgenerated for one of the plurality of angles; determining, for eachangle of the plurality of angles, the tracking quality metric value forthe first time based on the analysis of the projection generated at thatangle and the first time; generating a second plurality of projectionsof the 4D treatment planning image for a second time of the 4D treatmentplanning image, wherein each projection of the second plurality ofprojections is generated for one of the plurality of angles;determining, for each angle of the plurality of angles, the trackingquality metric value at the second time based on the analysis of theprojection generated at that angle and the second time; and selectingthe subset of the plurality of angles from which to generate thetracking image of the target during the treatment stage based on thetracking quality metric values at the first time and the trackingquality metric values at the second time.
 20. The method of claim 1,further comprising: generating a motion model of the target; estimatinga future motion of the target based on the motion model; determining anestimated future position and orientation of the target based onapplying the estimated future motion to the target in the treatmentplanning image; determining, for each angle of the plurality of angles,a second value of the tracking quality metric for tracking the targethaving the estimated future position and orientation; and selecting asecond subset of the plurality of angles from the subset, wherein thesecond subset includes angles with a second tracking quality metricvalue that satisfies the tracking quality metric criterion.
 21. Themethod of claim 1, wherein the treatment planning image is afour-dimensional ( 4D) treatment planning image, wherein determining thetracking quality metric value for a particular angle of the plurality ofangles comprises: generating a motion model of the target from the 4Dtreatment planning image; and determining, from the motion model, anamount of motion that is detectable for the particular angle, whereinthe tracking quality metric value is based at least in part on theamount of motion that is detectable.
 22. The method of claim 1, whereinthe imaging device is an imaging device of a radiation therapy apparatushaving a first additional imaging device with a first fixed angle and asecond additional imaging device with a second fixed angle, wherein theplurality of angles in the subset are separated from the first fixedangle and the second fixed angle by at least a minimum angle separationthreshold.
 23. The method of claim 1, wherein determining the trackingquality metric value for a particular angle of the plurality of anglescomprises determining a field of view (FOV) for a particular projectionassociated with the particular angle, wherein a particular trackingquality metric value for the particular angle with a FOV that is below aFOV size threshold fails to satisfy the tracking quality metriccriterion.
 24. The method of claim 23, further comprising: determiningthat there are an insufficient number of angles in the subset of angles;and outputting a suggestion that the patient be repositioned to causethe target to be closer to an isocenter of a helical delivery radiationtherapy apparatus that comprises the imaging device.
 25. A computingdevice comprising: a memory; and a processing device operatively coupledto the memory, the processing device to: determine a plurality of anglesfrom which tracking images can be generated by an imaging device;generate a plurality of projections of a treatment planning image of apatient, the treatment planning image comprising a delineated target,wherein each projection of the plurality of projections has an anglethat corresponds to one of the plurality of angles from which thetracking images can be taken; determine, for each angle of the pluralityof angles, a value of a tracking quality metric for tracking the targetbased on an analysis of a projection generated at that angle; and selecta subset of the plurality of angles that have a tracking quality metricvalue that satisfies a tracking quality metric criterion, one or moreangles from the subset to be used to generate a tracking image of thetarget during a treatment stage.
 26. The computing device of claim 25,wherein generating the plurality of projections comprises generating aplurality of digitally reconstructed radiographs (DRRs), and wherein tothe processing device is to: generate a target region DRR for aparticular angle of the plurality of angles based on tracing rays at theparticular angle through a region of the treatment planning image thatincludes the delineated target, wherein additional regions of thetreatment planning image that do not include the delineated target areexcluded from the traced rays; generate a standard DRR for theparticular angle based on tracing rays at the particular angle throughthe treatment planning image; and determine the tracking quality metricfor the particular angle based on searching for the target in thestandard DRR using the target region DRR based on a target trackingalgorithm that will be used to track the target from tracking imagesduring the treatment stage, wherein a failure to identify the target inthe standard DRR from the searching causes the particular angle to havea particular tracking quality metric value that fails to satisfy thetracking quality metric criterion.
 27. The computing device of claim 25,wherein the treatment planning image comprises a computer tomography(CT) scan, and wherein to generate a projection of the plurality ofprojections for a particular angle of the plurality of angles theprocessing device is to: trace a ray through the CT scan at theparticular angle, wherein the ray passes through the delineated target;accumulate CT values for the ray as the ray traverses the CT scan; andgenerate an effective depth value based on an accumulation of the CTvalues, wherein the effective depth value represents a total accumulateddensity of material traversed by the ray, wherein the tracking qualitymetric criterion comprises a maximum acceptable effective depth value.28. The computing device of claim 25, wherein the treatment planningimage is a three-dimensional (3D) treatment planning image, and wherein:the target comprises a plurality of fiducials; to generate a projectionof the plurality of projections for a particular angle of the pluralityof angles the processing device is to project positions of the pluralityof fiducials from a 3D space of the 3D treatment planning image at theparticular angle onto a two-dimensional (2D) virtual detector plane; andto determine the tracking quality metric for the particular angle theprocessing device is to determine whether a first fiducial of theplurality of fiducials overlaps a second fiducial of the plurality offiducials in the virtual 2D detector plane, wherein an overlap betweenthe first fiducial and the second fiducial causes the tracking qualitymetric value for the particular angle to fail to satisfy the trackingquality metric criterion.